SaasOffers

AI visibility tracking is becoming essential. I'm launching an AI tool for small businesses and struggling with the same discovery problem — how do people find you when you have zero audience? Curious how you're measuring AI-driven traffic vs traditional search.

I believe social media gives us a false sense of security. There are so many posts and videos about people who created an app and now make 80K a month, but in reality, most of it is just clickbait. The SaaS field feels overbuilt. In document tools especially, which NexusDocs focuses on. The first objection is always the same: I can paste this into ChatGPT. That objection is fair. A generic summary is not a company or a proper response. What saturation hides is that most of those tools do the same job: explain the text. They do not own a moment. They do not change a decision. They do not get a second document without a reminder. AI lowered the cost of version one. It raised the number of lookalike products. The scarce things are still the same: I’m building through that, not around it. Shipping was the easy week. Getting one person to use it is the actual business.

The part about connecting AI visibility to actual business outcomes is what interests me most. Being mentioned or cited is useful, but the harder question is what happens after that visibility.

I’d be interested in seeing this measured as a funnel: AI mention → click/visit → qualified action → conversion. Otherwise, AI visibility could easily become another metric that looks impressive without showing whether it actually influences revenue.

The live Reddit-thread angle is particularly interesting because it gives companies something actionable to improve rather than just another visibility score.

The Reddit finding matches what I see when I check why one company gets cited over another: the engines lean on threads and third-party pages because that's where claims look independent. A company's own site says whatever it wants; a thread where three users name the same tool unprompted reads as verified. The part most people skip is boring: get specific, checkable claims (numbers, comparisons, real customer outcomes) onto pages you don't own. One caveat from the testing side - what ChatGPT cites and what Perplexity cites overlap less than you'd expect, so a thread that wins one engine can be invisible to the other. Worth running the same buyer question through both before deciding where to spend effort.

The measurement half is solid. The reply half is where this gets people banned, because mods remove vendor answers fast and a shadowbanned domain is a worse outcome than being unmentioned, so the feature that matters is not whether the thread is open but whether that subreddit tolerates a vendor reply and whether the account is old enough to survive one. The citations that hold up over time come from original data nobody else has, since models keep quoting the source of a number long after a thread has scrolled away.

This is fascinating — and slightly terrifying for someone building in the exact opposite direction.I built an offline-first AI assistant. Nothing ever leaves the device. Zero API calls. Zero cloud.Which means: I will never appear in ChatGPT or Perplexity answers. Not because my product is bad, but because by design, there is nothing to scrape, nothing to index, nothing to cite.Your tool made me realize: I'm not just invisible to traditional SEO. I'm invisible to the next SEO. The one where AI answers replace Google results entirely.The irony? The whole selling point is "your data stays private." But "my product stays private" too — and not in a good way for discovery.Have you seen any offline-first or privacy-first products successfully break into AI-generated answers? Or is this a structural blind spot we'll have to solve differently?

Congratulations on the launch! One thing I noticed is that your homepage explains the features before the problem you’re solving. Many first-time visitors decide within a few seconds whether to stay. Leading with the customer’s pain point could increase engagement. Happy to share a few more ideas if that’s useful.

I also develop the free security first pdf tool name aeropdf .app but the issue is even after one year the traffic is very low and also the search engines give very low ranking.

I’m also planning to build a SaaS product after optimizing the project I’m currently working on.

Adjacent disclosure, I work on an SEO tool, so read this as someone in the same neighbourhood rather than a neutral party.

Building on ohad1976's removal point, because the mechanism is worse than it looks and I only know that because we walked straight into it.

Reddit filters at the account level, site wide, on something called Contributor Quality Score. Individual subreddits set automod to auto reject anyone below a threshold, and you are never shown your score anywhere in the interface. A newish account replying in a category thread is close to the worst possible risk profile for that filter.

The part that makes it a product problem rather than a customer problem: a removed comment looks completely normal to the person who wrote it. It sits there on the thread when they view their own profile. The only signal is an automod reply, and that lands under Notifications rather than the inbox, so if you are not specifically looking for it you will not find it. We lost two days before noticing that every comment we had left in one subreddit had been removed within seconds of posting.

So a tool reporting replied in 30 relevant threads can be reporting 30 while the number visible to an actual human is zero, and neither you nor your customer would know. To answer ohad's question directly, I would not just track removals, I would make survival the metric. Check each reply from logged out a few minutes after posting and report survived rather than posted. It is cheap, and nobody in this category does it.

One other thing worth separating in the pitch: whether a Reddit mention feeds a citation depends on which mode the assistant is in. Browsing assistants do live retrieval against an index, so a fresh thread can surface quickly. Non browsing answers come out of training data and move at the next model cut, months away. Both are real, they run on completely different clocks, and customers will judge you on the fast one while part of what you are selling sits on the slow one.

Have you seen the removal problem in customer accounts yet, or has it not surfaced precisely because nobody can see it?

One thing I’d separate is “am I mentioned?” from “am I mentioned in the prompts a buyer would actually ask right before choosing?” A tiny test: keep 20 fixed prompts split into compare / alternative / pricing / use-case buckets, run them weekly, and only count a win if the answer gives a real reason to pick you. Otherwise the visibility score can move up while the buyer still has no reason to click.

Spot on regarding the LLM citation feedback loop. How do you distinguish between high-intent recommendation threads versus broad, casual mentions when scoring visibility?

Great breakdown, super interesting approach to AI visibility. Good luck with the growth!

Interesting, good article.

The line between tracking and getting cited is the real insight here, most AEO tools stop at the dashboard. But running aged accounts with fake personas across Reddit threads is the same failure mode I've seen on other platforms: once the network gets flagged, every account in it goes down together, not just the newest one. I'd bet on training the actual team to answer three threads a week in their own voice, slower, but nothing there for a platform to detect.

Interesting approach. I’m currently developing a Morocco travel project for the U.S. market, so I’m especially interested in how AI visibility can help small travel businesses get discovered. Thanks for sharing.

Spot on regarding Reddit being the backbone of modern RAG pipelines. Ever since OpenAI and Google inked data licensing deals with Reddit, brand presence in fresh threads has become prime real estate for LLM retrieval. Connecting passive AEO tracking to real-time Reddit engagement actually addresses the root cause of why brands get omitted. Curious—how are you handling thread freshness vs. sub-reddit self-promotion rules when alerting founders to jump in

Really interesting approach. I especially like the idea of moving beyond simply tracking AI visibility and actually working on getting businesses mentioned in relevant conversations.

I’m also working on an online home improvement and interior design platform, Interior Glamour, where we’re building useful, topic-focused content around areas like flooring, lighting, home decor, and interior design. It’s interesting to think about how AI visibility and citations can help this kind of niche content reach the right audience.

The “gets you cited” part is more interesting than simply tracking visibility. How are you measuring whether a citation is actually useful versus just appearing in the answer?

That's very convenient

A lot of startup perks are hidden in plain sight. I found the same with cloud credits and dev tools. You usually discover them when you already need them.

Good find.

I'm not a big fan of Notion, and we currently use Whimsical.com - the Notion editor is just way too buggy for me.

I've recently come across Outline https://www.getoutline.com/ and it looks good - don't have time to test!

A lot of startup perks are definitely hidden in plain sight.

Founders usually hear about the big ones like AWS credits, but there are many smaller tools offering startup programs that can make a big difference when you're trying to keep costs low.

The interesting thing is that early-stage founders often don't need more tools — they need the right tools that improve collaboration and execution.

I’ve found that building a simple stack early (docs, project management, CRM, analytics, automation) saves a lot of headaches later when more people join.

Would be interesting to see a community list of underrated startup programs beyond the usual ones.

Startup perks are definitely one of those things most founders discover too late.

A lot of companies advertise the big ones (AWS, Stripe, HubSpot, etc.), but there are tons of smaller programs that can save money during the early stages.

One thing I’ve noticed is that founders often focus on discounts but underestimate the value of tools that improve execution — project management, documentation, analytics, CRM, automation, etc.

The best startup stack is usually not the most expensive one, it’s the one that helps you move faster with a small team.

Curious what other underrated startup programs people have found besides the usual list.

Thanks for sharing

Microsoft for Startups is the one I see founders sleep on most. SocialPost.ai is a member, and the Azure credits plus partner network access were worth more than the better-known AWS and Stripe perks. One caution on all of these programs: treat credits as runway for validation, not a reason to build on infrastructure you can't afford at full price.

This is a solid find! People always talk about AWS and Google Cloud credits, but SaaS perks for day-to-day operations like Notion often get overlooked.

Another underrated perk I’ve seen recently is Mixpanel for Startups (up to $50k in credits) and Segment’s Startup Program, both are huge lifesavers when you’re trying to track early user behavior and funnel conversion without burning cash.

Also, a lot of people don't realize HubSpot for Startups offers up to 90% off in your first year if you're associated with an approved incubator or accelerator, which makes a massive difference for early sales outreach.

Appreciate you sharing this! Definately going to look into the Notion AI business perk for our team workflow.

Nice!

Per-seat SaaS pricing is the absolute silent killer for solo founders. You start adding a few contractors, and suddenly your monthly burn rate spikes. Great find on the Notion perk!

To answer your question about underrated benefits: honestly, the biggest "startup perk" I’ve leveraged isn't an official program at all—it's Cloudflare's free tier.

I currently run my entire SaaS backend (Vitabase) and a heavy n8n automation pipeline on a "split-brain" architecture. I use my local workstation for the heavy computational lifting and tunnel it to the web using Cloudflare Zero-Trust. Total server cost? Exactly $0.

Founders usually default to burning through AWS credits or paying premium managed database fees right out of the gate. But if you know how to set up secure local-to-cloud tunneling, you can bootstrap massive workloads for free without even needing to apply for a credit program.

Out of curiosity, how are you finding Notion AI? Is it actually replacing standard ChatGPT/Claude in your daily workflow, or just augmenting it?

Nice concept. One thing I'd be curious about is how you verify that partner offers are still active and updated. Keeping startup deals accurate over time seems like one of the biggest challenges. Wishing you the best with the project!

honestly the underrated part isn't the notion perk, it's that most of these programs are only discoverable by bumping into someone at an event. and yeah, per-seat pricing is what quietly kills solo tools the second you add one collaborator.

How are you finding Reddit groups that allow you to mention your product?

This makes a lot of sense. I think the biggest mistake is treating Reddit as a volume game instead of figuring out which discussions actually influence the answers people see. A few genuinely useful comments in the right threads can be far more valuable than hundreds of generic posts scattered across unrelated subreddits. The part about tracking competitor mentions is especially interesting, because it gives you a much clearer idea of what the models are actually picking up. I’d definitely recommend checking the data and about how this works before spending time posting blindly.

The thread-level targeting makes sense, identifying the pages AI engines already cite is much more useful than blindly posting across entire subreddits.

I’m curious about the attribution, though. When the compliance brands saw increased traffic after two weeks, how did you separate traffic caused by Reddit/AI mentions from other SEO or marketing activity?

Also, do you find that participating in already-cited threads works better than creating a stronger new post targeting the same query? I’m currently testing this for SongTrailer, so that distinction would be useful.

Really enjoyed this, thanks for writing it up. I hadn't thought about AI answers pulling from just a handful of Reddit threads instead of Reddit as a whole, that reframes things a lot. The point about comments needing to hold up even with the brand name stripped out is a good one. Feels like a useful gut-check for genuine vs. promotional, not just for Reddit but anywhere you're trying to be helpful online.

Thanks for sharing what's working, posts like this are genuinely useful for the rest of us still figuring this stuff out. Did you stumble onto this pattern by accident, or were you specifically looking for it?

You're right about the scarcity, and it's the part most people miss — the model pulls from two or three threads per query, so posting everywhere is dead on arrival. A tool that surfaces the exact URLs getting cited for your prompts is genuinely useful intel.

The done-for-you persona campaigns are where it eats itself, though. Those threads only get cited because they read as real human consensus. Once there's an industry quietly seeding brand mentions through aged accounts, that's exactly the signal the models start discounting — you're strip-mining the thing that makes the channel work, until it doesn't. And "comments that stand on their own with the brand stripped out" describes a genuine comment. If it's that good, it doesn't need the aged persona. That layer only exists because the mention is the actual point, not the contribution.

This is really interesting. Am I understanding it correctly that we could ask AI the questions we care about, look at the Reddit threads it references, and then contribute genuinely to those discussions? I’m curious how AEORank makes that process more reliable or scalable.

I think the biggest takeaway here isn't just Reddit; it's understanding where AI gets its answers from. A lot of people assume publishing more content automatically increases visibility, but if AI is citing a small set of trusted discussions, then contributing meaningfully to those discussions becomes much more valuable than posting everywhere. It'll be interesting to see how this evolves as AI search continues to mature.

The insight about AI pulling from a handful of threads is right, and it matches what I see with SocialPost.ai showing up in AI answers. The part I'd push back on is the aged-accounts-with-personas play: you're renting a moat that disappears the moment Reddit or the model providers get better at detecting it. Real customers arguing for you in those threads is slower, but it's the only version that compounds.

The "small handful of threads" insight is the key thing most people miss. I've been watching this pattern with CalculatePilot (free calculator site I just launched) — our llms.txt and structured data are solid, but the AI citation game is really about being in the right specific thread when the model was trained or when it's pulling live results.

The AEORank angle of showing exact cited URLs rather than just "post on Reddit" is genuinely more useful than the generic advice. Curious whether you're seeing different citation patterns between ChatGPT (which pulls from training data mostly) vs Perplexity (which does live web retrieval) — because the strategy for getting cited by each seems meaningfully different.

For a tool-heavy site like mine the bet has been on structured data + llms.txt to get cited directly rather than via Reddit, but I'm watching whether that holds as AI search matures. Have you seen calculator/tool sites show up in your citation data, or is it mostly content/advice pages?

Interesting approach. As AI search evolves, visibility will depend less on publishing more content and more on being cited in trusted discussions. Companies like GeekyAnts, Thoughtworks, and EPAM seem well-positioned because of their strong technical content footprint.

the 'five threads, not all of Reddit' framing makes a lot of sense, explains why people post everywhere and see nothing. curious how you handle threads that get buried after a week or two, does citation stick once a model picks them up or does it drift back out if the thread goes quiet?

The hardest part isn't posting on Reddit. It's finding the threads AI actually keeps citing. That's a much smaller target than most people think.

The interesting part here is that once you turn something like this into a done-for-you service, the bottleneck probably shifts from finding the opportunity to executing consistently across clients.

Curious, what part of running these campaigns takes the most manual effort right now finding opportunities, creating the content, reporting, or managing client communication?

The core observation is genuinely sharp — the model

isn't reading all of Reddit, it's pulling from a tiny

handful of threads per query, so blanket posting

everywhere is wasted effort. Knowing *which specific

threads* an engine cites for your space is a real

insight most people miss. That's a legitimately useful

reframe.

The part I'd be careful about — and this is just my

honest read — is the aged-accounts/personas angle.

Reddit's whole trajectory right now is cracking down

on exactly that, and even when comments "stand on

their own," the account pattern is what gets flagged.

I've been growing in these communities the slow,

genuine way (real account, real participation)

specifically, because the manufactured version feels

one policy changes away from getting torched.

Genuinely curious though — for the targeting part, once

someone knows the specific threads being cited, do you

find it works just as well with genuine first-person

participation as with the done-for-you personas? Because

the "know where to show up" half seems durable in a way

the "manufacture who shows up" half might not be.

I love the idea. A small amount of targeted effort beats "spray and pray" any day.

Looks good, will take a look and let you know about my feedback

This matches what I've seen too — the "post everywhere and hope" approach to Reddit/AEO almost never works because the model is only pulling from a handful of threads per query, not the whole subreddit. The harder part in my experience is that those winning threads shift over time as new discussions get indexed, so it's not a one-time audit, it needs ongoing monitoring. Curious how often you re-run the thread discovery for a given client — weekly, monthly?

This is the part most people miss. It is not about “being on Reddit” in general, it is about showing up in the exact threads that actually shape the answer. If your comment would not still sound useful with the brand name removed, it probably will not move anything.

I actually tried your tool, landing page is solid and clearly communicates the idea, but the dashboard needs improvement in terms of clarity and usability. Also, the pricing feels a bit heavy upfront, especially with no real free way to properly test the core value. Right now it’s hard to judge if the tool is worth it without being able to experience the main feature in action first.

the insight about AI engines pulling from a handful of threads rather than all of Reddit is the key framing here. most people still think of it as "post more = more visibility" when the actual mechanism is citation-specific.

we're seeing something similar with how people approach AI skill-building. everyone thinks volume (more courses, more tools) equals proficiency, when really it's about being in the right conversations and having the right depth on specific topics. precision beats volume in both SEO and skill development.

Reddit is one of the most indexed sites on the internet, which is why its extremely underrated(but risky) to promote your saas on there

Interesting perspective. AI search visibility is becoming a new part of SEO. I think the biggest challenge is not just getting mentioned, but actually creating useful discussions and resources that AI systems consider valuable.

The point about focusing on the specific conversations that influence AI answers instead of randomly posting everywhere makes a lot of sense.

The diagnostic half of this is the real business: showing founders the exact five threads an answer gets built from is worth paying for on its own. The done-for-you persona side is where I'd be careful as a buyer. The account farm is a rented asset, and the brand takes the downside if it burns.

Congrats on the launch! The design looks super clean and straightforward. Building in public is definitely the best way to get early feedback. Wishing you the best of luck with traction and users!

The “AI cites a tiny slice of Reddit” framing matches what I’ve been seeing too. Blind posting everywhere is mostly noise if you’re not in the threads models already trust.

Useful angle: showing which URLs get cited for a prompt, not just “be on Reddit.” For a compliance tool case, was the lift mostly branded queries, or problem-space prompts (“how do I…”) where the Reddit thread was the answer body?

Reverse-engineering AI search citations back to the specific Reddit threads ChatGPT and Perplexity pull from is brilliant. Targeting those exact high-ranking threads instead of blindly posting across subreddits is top-tier GEO (Generative Engine Optimization). Great work!

The placement half of this I buy. One lever on the other half that nobody in the thread has mentioned, and it's the highest return per minute I've found: IndexNow.

A lot of the AI answer surface reads from indexes you can push into rather than wait on — Copilot sits directly on Bing, and plenty of assistant and agentic-browser stacks query Bing or Brave search APIs underneath. Bing accepts IndexNow submissions: get a key from Bing Webmaster Tools, host it at your domain root, then POST your URLs on every publish. Pages land in hours instead of whenever a crawler wanders by. One key per domain, one script wired into the deploy step, done in an afternoon. Somebody above mentioned llms.txt and robots.txt, which is the same family of problem — but those are passive, they only pay off when a crawler eventually shows up. IndexNow is the one where you get to initiate.

Why this supports your thesis rather than competing with it: the thread is what gets you named, but there's usually a verification hop to your own site before a model will actually recommend you to someone. If your pages aren't in the index that hop comes back empty, and you can lose the citation even though the mention was sitting right there. Placement gets you into the conversation; being indexed is what lets you survive the fact-check.

Also worth knowing it's not Bing-only — the same IndexNow ping fans out to Yandex, Seznam and Naver, which matters if any of your buyers aren't in English-speaking markets.

As someone working on SEO for a font conversion tool, this is a useful perspective. AI visibility is becoming just as important as traditional search.

Interesting problem — the "which 2-5 threads is the model actually citing" framing makes sense given how retrieval-augmented answers work.Curious about the mechanism though: are you inferring the source threads from the citations/links models like Perplexity surface directly, or sampling a bunch of prompts and reverse-engineering which pages keep showing up? Those give pretty different reliability guarantees, and the second one seems like it'd drift as the underlying index/ranking changes.

Also, genuine question on the "aged accounts with real personas" part of the campaign side — isn't that pretty squarely against Reddit's ToS on inauthentic/coordinated behavior? Feels like it'd be a real risk (ban, or worse for the brands you're running it for) even if the comments themselves read as organic individually.

The useful distinction is not “Reddit matters,” but that only a small set of source pages matter for each prompt. I’d still be careful with attribution, though: a traffic increase after two weeks is promising, but the strongest proof would connect specific thread placements to citation changes across tracked prompts and models.

This is genuinely useful — most AI visibility advice stops at "post on Reddit" without explaining which threads actually matter. The idea of finding the exact URLs being cited is a smart shortcut. Curious how quickly the cited sources rotate — does AEORank track changes over time, or is it more of a snapshot? Also the point about aged accounts and standalone comments is something most people skip and then wonder why they get flagged. Good stuff.

Interesting results. I’ve also noticed that helpful discussions on community platforms can appear in AI search results. The key seems to be sharing real experience instead of posting promotional links. Did Reddit traffic also improve your Google rankings, or mainly bring direct clients?

The placement side you're describing (which threads AI actually pulls from) is one half of this. There's a technical floor underneath it that's worth checking too: a lot of sites never get crawled or parsed in the first place regardless of how good the placement is — blocked in robots.txt, no llms.txt, JS-only content a crawler can't read. Doesn't matter how many good threads mention you if the model can't read your own site when it goes to verify.

Built a free checker for that specific piece (no signup, called AI Visibility Score, findable on Product Hunt). It doesn't touch the placement/Reddit question at all, just whether a crawler can technically reach and parse what's already on your site.

The underlying insight here is solid — AI models don't index "Reddit" as one blob, they reference specific threads that become authoritative for specific queries. Knowing exactly which threads matter for your niche is genuinely useful intelligence.

The compliance angle (aged accounts, no cross-commenting, no link dumps) is the right guardrail. I've seen the alternative play out — it works for about two weeks until the community catches on and you've burned the account.

Curious about one thing: how stable are the referenced threads over time? If ChatGPT pulls from a specific Reddit thread today, does it still reference that same thread in 3–6 months, or does the model's context window shift as newer threads accumulate? That'd determine whether this is a "plant and harvest" strategy or more of a continuous presence play.

Went and checked aeorank.tech's FAQ myself. It says: "We do not run vote rings or operate fake accounts." That directly contradicts what you're describing above — "aged accounts with real personas, no cross commenting." Which is it?

Also tried to find anything on the Ticket Tailor / Reddit citation story and the "compliance software company" example — couldn't find a single thread, screenshot, or link for either. Not saying they don't exist, just asking: can you actually show them?

This is encouraging to read. I'm right at the start — just shipped a free tool

and I'm hitting the classic cold-start wall (near-zero reach on X, new-account

gates on basically every platform).

Genuine question: when Reddit started sending you clients, was it from posting,

or from commenting and being active in threads first? Trying to figure out where

the real leverage is when you're starting from zero reputation.

Interesting perspective! Being active in the right Reddit discussions definitely seems more valuable than posting everywhere. Thanks for sharing your experience.

Posting everywhere is easy but showing up where AI actually looks is a completely different strategy.

The harder part is still creating comments that people would find useful even if the brand name was removed.

That is probably what makes the difference over time.

Very good idea. It's creative.

Bekir's question is the one I'd most want answered too — and answering it actually explains why the diagnostic half of this is the strong half.

I'm in the middle of exactly this right now: warming up a real account, just being useful in threads, no product mention at all. The reframe that's working for me is that you don't "add value without being promotional" — you drop the promotion entirely and let being the most useful answer in the thread be the whole play. It lines up with what you said about comments that still stand if you strip the brand name out. On an older thread a fresh comment only earns its spot if it adds something the existing answers missed; restating them with a link bolted on is exactly the part that reads promotional and gets flagged. So mention the product only when it's literally the answer, and usually only when asked.

That's also why "here are the five threads AI is citing, go be genuinely helpful in them" is such a good use of the tool on its own — it points a real person who knows the product at the exact right rooms. That part I'd pay for. The done-for-you personas are the riskier bet Hariom flagged, and the downside isn't symmetric (a ban costs an account; a burned brand in a callout thread becomes the top result for the query you were trying to win) — but the diagnostic half doesn't need the account farm attached to be worth it.

Interesting approach. It would be more useful to find out which threads AI quotes, rather than posting everywhere without thinking.

Timing is wild — I just launched my first iOS app today and was literally sitting here trying to figure out my Reddit strategy this afternoon. Ended up learning that new accounts get filtered hard in most subreddits, so I'm doing a 3-5 day warm-up first.

The framing of "get into the specific threads that AI is already citing" is really smart framing — I hadn't thought about it that way. Most advice I read is "post in relevant subreddits" which is way too broad.

One thing I'm still unclear on: once you identify a thread the AI is citing, how do you add value without it reading as promotional? Especially on older threads where a fresh comment stands out. Any pattern you've seen work vs. get flagged?

This is a great idea. Can save hours and hours of wasted time trying to get the business into the discussion.

it is a nice approach i will try

The "it's five threads, not all of Reddit" framing is the useful bit

here, and I think it's underrated. Most people treat AI visibility as

a volume problem when it's closer to a placement problem — being in

the specific sources that get retrieved, not being everywhere.

The measurement side makes sense to me. Knowing which URLs an answer

is actually built from is a real gap, and "go participate in those

threads with something worth reading" is a reasonable conclusion to

draw from it.

The done-for-you part is where I'd want to understand more. Aged

accounts with personas is the thing Reddit moderators are actively

hunting, and the failure mode isn't just a ban — it's the client's

brand name attached to a public callout thread, which is a worse

search result than the one they were trying to fix. How are you

thinking about that downside for the client rather than for the

account?

Genuinely asking, because the diagnostic half of this seems solid

enough to stand without it.

That makes sense—Reddit can be a powerful source of AI search visibility because AI systems often surface authentic discussions and community recommendations. If your helpful Reddit contributions mention your expertise or brand naturally, they can increase trust, visibility, and potentially bring qualified clients to your website.

Great share. It's a good reminder that being part of relevant conversations can matter as much as traditional SEO now.

Amazing! Congrats.

I'm aware that Reddit is used widely in foundation model training, but using that to do AEO is so obvious that I'd be concerned this will no longer work as a marketing channel. That's my main concern - plus the issue of ensuring you're really adding value at the same time and not just spamming Reddit.

Any thoughts on that?

The two-week turnaround is the part I'd want to poke at, because that's faster than I'd have guessed.

I run a scanner that reads Reddit for buying-intent threads and I pulled 30 days of it last week: 147 threads, 78 subreddits, 8 products. One thing in there might complicate the picture. The threads with the strongest buying intent were consistently the quiet ones. High intent (60+ on our scoring) averaged 12 upvotes. Low intent averaged 57. Roughly a 5x gap, in the opposite direction from what you'd assume.

Which makes me wonder whether the threads that feed AI citations and the threads where someone is actually deciding are even the same threads. A 400-upvote "best Eventbrite alternative" megathread is a great citation source and a terrible sales conversation. The 6-upvote one where a nonprofit is asking about fee structures is the reverse. Both matter, but you'd work them completely differently.

Did the AI referral traffic convert, or just show up? That's the number I'd want.

(On accounts surviving - I automated the reading, then got greedy and tried automating some of the replying, and got shadowbanned promoting a Reddit tool on Reddit. Learned that one the expensive way.)

This tracks with something that just happened to us today, live. Posted a genuinely helpful, non-promotional reply to a real question in r/SEO from an account that existed but had basically zero history — auto-removed within minutes, "low CQS score," invisible to the actual thread. Content had nothing to do with it. The account had no standing, full stop.

What you're describing is the fix, not a hack: aged accounts, real karma, a persona that's consistent enough to be trusted rather than flagged. That's a genuinely different cost structure than "post good comments and hope" — it's infrastructure, not copywriting.

One thing I'd be curious about: how do you handle the tension between "aged account with real history" and "brand only ever goes where it genuinely fits the thread"? Feels like the second constraint is what keeps the first from decaying into karma-farming, but it also caps how fast you can scale accounts vs. how fast you find matching threads.

Interesting seeing Reddit become the bridge between search and AI answers. The hard part is not posting more, it’s finding real conversations where the product actually fits.

Really useful writeup, and the "only answer where it is genuinely the answer" rule is the part I would underline too.

One technical thing I ran into last week that fits right under this: even after the mentions exist, the AI crawlers have to be able to read your own site, and mine quietly could not. My robots.txt was inviting them in, but a bot-management rule at my CDN was returning 403 to the actual AI user agents. I only caught it by requesting my pages as those exact bots: a made-up bot name got 200 while the real AI ones were blocked, which is how I knew it was targeting them specifically. On top of that my localized content was injected with JS, so the crawler saw the wrong language entirely.

So before investing in the mention layer, it is worth fetching your own pages as the AI bots and confirming they get real, readable HTML back. Otherwise the citation you worked for lands on an empty shell. Curious whether you check crawlability as part of the AEORank audit.

Using aged accounts with separate personas is the part that makes me hesitate, because it can cross from useful participation into coordinated astroturfing very quickly. A more defensible moat would be helping clients earn genuine mentions from independent customers and communities, even if that is slower and harder to scale.

the "honest answer to a real question" constraint is doing all the heavy lifting here. most people trying to game AI citations skip that part entirely and wonder why their posts get nuked. the uncomfortable truth is that this only works if the product is actually good enough that recommending it is genuinely helpful — which filters out like 90% of the people who'll try to copy this playbook

Really cool to see this play out with actual data, the "honest answer to a real question" caveat is the part I'd bookmark, feels like the whole thing hinges on that.

We've been doing something similar with our own Reddit presence for trimy.io, just genuinely answering questions in smaller communities without pitching anything, and it's slow the way you describe, no shortcuts, just showing up. Good to hear it can actually move the needle on AI citations too, that's a nice bonus on top of the community goodwill.

Appreciate you sharing the real mechanics instead of just the headline result, this is the kind of post that's actually useful to bookmark. Good luck with AEORank!

The part I’d be careful with is measuring “cited in AI” separately from buyer intent. I’d split the prompts into three buckets: job-to-be-done searches, competitor/alternative searches, and curiosity/research searches. The competitor/alternative bucket is usually the one that tells you whether this is moving actual purchase behavior, not just creating a nice screenshot.

The "fresh accounts get nuked" part is painfully real — I had two posts removed by Reddit's filters just this week (new account + new domain link = instant spam signature, apparently the account age matters more than the content).

The observation about AI answers being sourced from Reddit threads matches what I see too, and it's a genuinely important shift. Where I'd push back a little: managed personas that "never cross-comment each other" is the part that would keep me up at night as a client. When coordinated networks get caught, Reddit tends to ban the brands they promoted, not just the accounts — at which point every citation you built becomes a liability. The slow version (real account, real name, only answering where you'd answer anyway) seems like the only one that compounds without that tail risk.

Curious how you think about that risk for clients — is there a disclosure line you won't cross?

My team is interested in using Reddit more aggressively, may try using this. Do you offer a discount for startups?

Spot on about Reddit being brutal. The moment a throwaway account drops a product link, it gets shadowbanned instantly, and LLM scrapers ignore low-karma spam threads anyway. Participating naturally where people are actually asking for alternatives is literally the only way to build citations that survive. AEORank looks like a really clean way to map out where those conversations are happening without doing it all in messy spreadsheets.

I signed up but the pricing put me off. I would suggest to have a free trial and keep the charges for comments and posts. Also, your biggest clients would be agencies like mine but $199 per website can be expensive.

Also, maybe the AI can pull the product/service description from the website itself for less friction?

Reddit showing up that fast tracks with what we've seen. We've been

building links for a pre-launch product and the pattern that surprised

us: platforms where you publish content into someone else's template

almost always wrap your links in nofollow, but platforms where you

control the raw HTML don't. Same tier of domain, completely different

outcome.

Curious whether the AI citation is coming from the Reddit thread itself

ranking, or from the client's own pages getting picked up as a result.

Those need pretty different follow-up strategies.

yeah reddit platform is really getting soo much views and users i also facing same thing when i post about my product first sale 2 more sale i got from reddit and i literally shocked when i see it get me soo much motivated to continue build and shipped new products

If you're looking for API-level access to track where AI engines cite your brand, there's actually a dedicated endpoint for this now at API Serpent's AI Rank API — it queries ChatGPT, Claude, Gemini, and Perplexity in a single call and returns a normalized citation score + source URLs.

The use case is exactly what you're describing: brand monitoring across LLMs, not just traditional search.

Hey, great story!

I'm also building an AI SEO tool right now and seeing how much Reddit influences AI answers. Quick question — how did you find those specific small communities (like event subs) to start posting in?

Good luck with AEORank!

the gap between "posting about your product" and "being the person who knows the answer" is everything on Reddit

Useful case study. The distinction I would make is between citation sources and buyer-intent sources.

A broad Reddit thread can teach an AI model that a product belongs in a category answer, but the quiet thread with a very specific workflow pain is often where the buyer is actually deciding. Those need different work: one is about being named accurately, the other is about reducing risk for a person with a job to finish.

For infra/payment/SaaS products, I think the landing page has to support both. It should expose concrete facts that can be cited: who it is for, who it is not for, pricing/fees, integration path, failure modes, security/reliability details, and comparison boundaries. Otherwise Reddit mentions may create visibility, but the model and the buyer still lack enough evidence to recommend or trust it.

The best signal would be: same prompt set before/after, citation movement, and then whether visits from those answers reached activation or conversion.

Interesting results. It's always valuable to see real data instead of assumptions. Thanks for sharing your experience.

This is a fascinating approach. The Reddit → AI citation loop makes total sense — I've been noticing the same pattern with my own product.

One thing I'd add: the "aged accounts with real history" point is crucial. I tried posting from a fresh account and got shadowbanned within days. The organic, genuine contribution approach is the only thing that actually survives long-term.

Curious — how do you handle niches where the Reddit communities are very small or highly moderated? That's the challenge I'm running into with content creators.

The way I see it is that AEO / AIO / GEO (whatever acronym you choose to call it) is all about amplifying the white hat tactics of SEO and ignoring the "hacks." Sure, FAQ schema can help (LLMs love structured content) but it's all about that EEAT at the end of the day.

And Reddit is definitely top tier when it comes to getting cited by AI answers...but you're right, it's a slow burn. I'm still dragging my heels on starting my Reddit engagement plan because I'm used to being a lurker, lol.

I am still waiting no response from any platform

There's a technical layer under this thread that nobody mentioned: even when the Reddit mentions exist, your own site has to be readable by the crawlers that follow them. Mine wasn't - JS-only SPA, so the AI bots saw an empty shell where all the landing copy should be. I only found out because I fetched my own pages the way their bots do. Prerendering fixed it. Worth checking before investing in the mention layer, otherwise the citations have nothing solid to land on.

On the persona debate in the comments: I'm doing the DIY version for my own product - one account, my real history, disclosure when I mention my own tool, and most comments with no product in them at all. Slower, but nothing to purge, and the discipline of "only answer where you'd answer anyway" keeps teaching me things about the product - the threads tell you what people actually can't do.

And the upvote inversion described above matches my (much smaller) experience: the quiet thread where someone describes their exact workflow pain is where a recommendation lands. The megathreads feed the models; the small ones feed the funnel. Different jobs, both worth doing.

There's a technical layer under this thread that nobody mentioned: even when the Reddit mentions exist, your own site has to be readable by the crawlers that follow them. Mine wasn't - JS-only SPA, so the AI bots saw an empty shell where all the landing copy should be. I only found out because I fetched my own pages the way their bots do. Prerendering fixed it. Worth checking before investing in the mention layer, otherwise the citations have nothing solid to land on.

On the persona debate in the comments: I'm doing the DIY version for my own product - one account, my real history, disclosure when I mention my own tool, and most comments with no product in them at all. Slower, but nothing to purge, and the discipline of "only answer where you'd answer anyway" keeps teaching me things about the product - the threads tell you what people actually can't do.

And the upvote inversion Telman describes matches my (much smaller) experience: the quiet thread where someone describes their exact workflow pain is where a recommendation lands. The megathreads feed the models; the small ones feed the funnel. Different jobs, both worth doing.

Hello, I’m a software engineer interested in building AI-powered SaaS products. I’m here to learn from other founders and developers, exchange ideas, and connect with people working on interesting projects. Nice to meet you!

Wow Thank You. This is refreshing.

This matches what I just learned the hard way. Product Hunt and LinkedIn sent zero users to my Chrome extension, while the useful conversations started with one specific workflow pain. The distinction in the comments between citation traffic and buying intent feels important. Did the client see signups or paid conversions from the AI traffic, or only visits? That would show whether Reddit created awareness or actual demand.

The two-week signal is real, but the aged-persona-account approach is the part that eventually breaks: platforms get better at detecting it, and one purge takes your citations down with it. I watched the same dynamic in the Microsoft partner channel for 20 years, whoever owns the trusted recommendation layer owns demand. The durable play is making your actual customers loud in those threads, not renting personas to do it.

That's cool, my issue is that all of my posts from reddit get auto-removed

This is a clean case study. The “product was good, AI just never said the name” part is the scary bit, because that used to be an SEO problem and now it’s a recommendation problem.

Reddit as the source layer makes sense too. Models remix what’s already trusted in the wild, so if you’re invisible in those threads you’re invisible in the answer. Turning the spreadsheet chaos into AEORank is the classic IH move, feel the pain by hand, then productize the loop.

I’m in a neighboring lane with Make it RAIN. Less “does ChatGPT recommend you,” more “once a builder has something shipped, how do they get buyers, pricing, and a 30-day path without drowning in tabs.” Built it after doing that mess myself: https://ReliableAINetwork.com

Testing whether links still get stripped on my account, so if this URL vanishes I’ll know I’m still gated. Either way, strong writeup. Two weeks to a visible AI referral jump is a hell of a proof point.

Today's Reddit DNS issue is another reminder of how quickly a small infrastructure problem can disrupt millions of users.

For engineering teams, it's not just about knowing that something is down—it's about understanding what changed, when it changed, why it changed, and how it affects the rest of your digital presence.

That's exactly why I'm building Stacck.

Stacck is a website intelligence platform designed to bring website auditing, change monitoring, and uptime intelligence into one unified workspace. Instead of piecing together information from multiple tools, teams can understand the health of their websites from a single platform.

I'm looking for 5–10 design partners who manage websites at scale and want to help shape the product.

I'm especially interested in working with:

- SaaS companies

- Digital agencies

- Web development teams

- DevOps and platform engineering teams

- IT departments

- E-commerce businesses

- Organizations managing multiple websites

As a design partner, you'll get direct access to the roadmap, influence product decisions, receive priority support, and help build a platform around real operational challenges—not assumptions.

If your team has ever dealt with outages, unexpected website changes, monitoring blind spots, or tool fragmentation, I'd love to hear how you solve those problems today.

If you're interested, leave a comment or send me a DM.

Website: https://stacck.vercel.app

#Reddit #DNS #WebsiteMonitoring #DevOps #SRE #PlatformEngineering #Infrastructure #Observability #SiteReliability #SaaS #Startup #BuildInPublic #DesignPartner #WebDevelopment #TechStartup #Stacck

Reddit's DNS outage shows how fragile website operations can be.

I'm building Stacck to unify website auditing, monitoring, and uptime into one platform.

Looking for 5–10 design partners to shape it.

https://stacck.vercel.app

#BuildInPublic #SaaS #DevOps #Stacck

Cool

The direct-partnership point below is right, but the thing that decides whether Deel renews this isn't the negotiation, it's attribution. If the credits run through a plain 'mention SaaSOffers' or a shared code, Deel can't cleanly see how many paying customers you actually sent, and partnerships quietly die at renewal when the partner can't measure ROI. Before you pitch the next SaaS company, I'd pin down exactly how each side counts a conversion: unique codes, a tracked signup link, or a shared dashboard. The pitch that lands isn't 'we have an audience', it's 'here's the revenue we drove you last quarter, want more?'. How are you tracking the Deel conversions right now?

is it realy easy to build a sass product i always want to become a developer and generate money like this.

The "saving money equals raising money" framing is something I wish I had heard earlier. Spent months chasing investors when cutting costs would have bought the same runway without giving up equity.Also interesting that you approached Deel directly rather than just plugging into their affiliate program. How many people said no before someone actually engaged with the partnership conversation?

Direct partnerships are much more valuable than a page full of affiliate links. If founders know the offers are negotiated, there's a better reason to come back.

"saving money is just as valuable as raising money" is a good line, and honestly underrated by most early founders.

the part i didn't expect: SaaS companies being that open to partnering with small communities. i always assumed you needed size first, good to know the value/audience angle works earlier than that.

nice to see a deal post that's actually a story and not just a coupon drop.

Love this. The strongest part for me is that SaaSOffers turns a boring but painful founder problem into distribution: everyone needs tools, everyone hates overpaying, and good deals create a natural reason to come back.

Also curious: when partnering with Deel, was the main leverage your existing startup audience, the quality of the deal page, or the founder-to-founder angle?

Really like the concept. Startup software costs add up quickly, especially for bootstrapped founders, so having verified deals in one place is genuinely useful. One suggestion would be to add more filtering options (by startup stage, category, or funding status) so founders can find the most relevant offers faster. Wishing you continued growth—looking forward to seeing how the platform evolves!

For founders who want to benefit from this offer you can use this link : https://saasoffers.tech/offers/deel

the "small but specific audience beats big generic one" point is real. I'm not even doing partnerships yet but already noticing the same thing with directories, some list barely sends traffic but the users are your exact people, versus stuff with way more reach but nobody who actually cares. easier to negotiate anything, funding or partnerships, when you can say "these are the 200 people who all have this exact problem" instead of "we have a big audience"

One thing I've noticed is that partnerships like this usually come from having a specific audience, not just a large one. A small community of the right founders is often more valuable to SaaS companies than a much bigger but generic audience.

nice

Congrats on getting the Deel partnership over the line, that's not a small thing to negotiate as a smaller platform. To your question about the biggest challenge building a remote team: from what I see on the hiring side, sourcing is rarely where teams get stuck. It's everything that happens after someone says yes, contracts, compliance, getting them actually paid on time in their local currency. Founders spend so much energy on finding the right person that the "how do we legally employ them" part becomes an afterthought until it's urgent. That's honestly what makes this Deel angle interesting to me, it's solving the part of hiring that doesn't get talked about until it's already a fire. Would be curious whether the founders you talked to brought up onboarding speed as a pain point too, or if cost was really the main driver for most of them.

This is a solid write-up. I really liked your point that saving money can be just as valuable as raising it especially for early-stage startups where every dollar extends the runway.

One thing I'm curious about: when you first approached Deel, did you lead with data about your audience, or was it more about building the relationship over time?

I'm building a startup myself, so I'm always interested in how founders land their first meaningful partnerships with larger companies.

this is actually pretty smart. i feel like everyone obsesses over raising money but barely talks about how much you can save by negotiating better software deals. how did you get deel to actually agree to the partnership

Your line about software companies being surprisingly open to partnerships matches 20 years I spent in the Microsoft channel: vendors will trade real money for a qualified audience because credits are cheaper than their blended CAC. That is also your negotiating lever with the next partner, show them conversion data from the Deel deal, not signup counts. The partner list compounds once you can prove one deal converted.

Nice work. I like that you talked to the company first instead of only using affiliate links. One idea that could make this even more useful is adding a short guide with every deal. Explain who should use it, who should skip it, and the real cost after the credits end. A few founder stories would help too. That makes it easier for people to decide if the offer fits their stage instead of signing up just because there is a discount.

The point about saving money being as valuable as raising money is underrated. Most early stage founders are so focused on the funding conversation they ignore the fact that cutting $1,500 in costs has the same effect on the runway as raising $1,500. The Deel partnership model is interesting too. Direct partnerships with SaaS companies rather than standard affiliate deals is a much better value prop for your audience. Curious how long the initial outreach to Deel took before they were willing to have a real conversation about it.

I agree with this statement: "Sometimes, saving money is just as valuable as raising money." SaasOffers seems like a great tool, and I think I’ll likely use it. I see that you used Reddit, and since the AI ​​is advising me to use it too, thanks for confirming what the AI ​​told me.

Nameless_Eternal here that was something good

That freemium insight is one of the clearest I've seen. Paying for the layer around the product, not the product itself. Saving that one. Congrats on the $3K, hope the PH launch went well.

That lesson about people paying for the layer around the deals instead of the deals themselves is probably the most valuable part of this whole post. A lot of products start as “access to information,” but the real value ends up being organization, trust, filtering, or helping people make decisions faster. Also getting 10k+ signups from Reddit organically is genuinely impressive. Curious to see how Product Hunt compares since the audiences behave so differently.

Congratulations on everything you've achieved. Nowadays, with AI and so many new tools being launched, it's increasingly difficult to gain recognition and clients, or not...

The line about 'a different problem for the same audience' is the single most important sentence in this post. Most freemium tiers just gate quantity (more storage, more API calls, more seats). The paid tiers that actually convert solve a new job-to-be-done that only becomes visible after the customer has used the free product for a while. At SocialPost.ai our paid tier is not 'more posts'. It is approval workflows, brand voice memory, and team review. None of which the free user needs until they have a real workflow. On Product Hunt: the day itself matters less than your launch list, your top three comments, and your follow-up the week after. Most launches die because the founder treats day one as a finish line. Good luck today.

What I found most impactful was the line 'It should solve a different problem for the same audience.'

When I was building my app it seemed like I was solving a problem for a different audience, and it made me question if certain implementations were actually worth the time or if they were just 'distractions'. I implemented a RSVP Reader, and it seemed a little off for me at first because it was just something I liked. However, I implemented it next to a 'Notes' section. I've received reviews from testers that applaud it's unique approach, but I'd like to see how the market feels. Those words gave me a change in perspective.

May your venture impact you and many more people!

congratulations on the launch, $3k MRR is inspiring and more inspiring is your grit in working through the process of acquiring customers. Wishing you the best

Congrats on the launch. The layer around the deals being the real sell is a great insight. People will pay for trust and tools, not just a discount code.

Congrats on the $3K MRR and the PH launch! It's really inspiring to see people succeed with SaaS. I hope to build my own profitable platform in the future too.

My congratulations! Well done!

Really interesting breakdown — especially the shift from “selling deals” to selling the layer around the deals.

The verified + comparison + workflow tools sound like what actually creates willingness to pay, not the discounts themselves.

Also impressive that Reddit alone carried both distribution and validation at this scale.

Curious how you’re thinking about Product Hunt — are you expecting it more as a traffic spike, or as a new acquisition channel long-term?

Congrats!!!

Congrats on the $3K MRR, clearing that milestone via the "utility layer" rather than just the deals is a huge validation of your product strategy. Product Hunt is a different beast, so watch out for the "tourist" signups vs. your high-intent Reddit crowd; keep your focus on the paid conversion data over the upvote count. Ready to see you climb the leaderboard!

The conversion insight here is underrated — "people paid for the platform, not the discounts" is exactly what good cohort analysis surfaces. If you segment premium converters vs. free users who never converted, the compare tool and alternatives pages show up disproportionately in the converter cohort. The deals were the acquisition hook; the platform layer was the retention and conversion engine.

Freemium products that stall on conversion usually have this backwards: they add more of the same for paid users instead of solving a different anxiety. For a deal platform the core anxiety is trust and verification, not deal volume — which is exactly what you figured out.

Congrats on $3K MRR. If you're building dashboards to track conversion funnels by cohort as you scale, this free SQL guide has query patterns useful for exactly this kind of funnel analysis: https://growthwithshehroz.gumroad.com/l/psmqnx

Hit the same realization at a different product. Started positioning my translation extension by listing features (three style modes, eight platforms) and watched users describe it back to me as a workflow tool, not a feature set. The shift wasn't adding a premium layer, it was renaming what the free tier even was. Verification and the compare tool are the same pattern at the buyer-experience level - solving the meta-problem of trust, not adding rows.

The 'paid tier solves a different problem' framing is the sharpest part of this, and most freemium founders never get there. On PH launches I've watched across portfolio companies, the first 90 minutes matter more than people expect. Comment volume on the page itself (not just upvotes) is what pulls hunters up the rankings, so reply fast to every comment, even one-liners. And have a clean before/after sentence ready when someone asks 'what does this actually do', because most launches die when the maker repeats the tagline instead of telling a story. Good luck today.

The “people paid for the platform, not the discounts” lesson is the strongest part here. It’s easy to assume the offer inventory is the product, but the real value is removing uncertainty: verified deals, comparison, tracking, and knowing what to apply for next. That’s a good reminder for freemium generally — paid should often reduce decision friction, not just unlock more rows in the database.

Really interesting approach. The distribution problem is something most indie hackers underestimate early on. Curious how you measured which channels were actually driving signups vs just traffic.

Lemzouri — congrats on the PH launch. Quick observation from the outside: 10,000 signups converting at roughly 0.38% to paid means 9,962 people saw enough value to give their email but not enough to pay $79. That gap between free signup and paid is almost always a positioning or trust problem on the upgrade page — not a product problem. That's exactly what I audit. Happy to find it if you're interested.

I'm curious, did you have a specific system for staying 'organic' on Reddit without getting flagged as spam, or was it mostly just manual engagement?

This is a great launch post. Thanks for pulling back the curtain on what actually drove premium conversions. This part really stood out: "What converted was the layer around the deals." I think a lot of founders default to the obvious paid feature set and miss what you caught—people pay to reduce complexity (compare tools, trackers, verification) not just get cheaper stuff.

One thing I'd love to understand deeper: once you realized the "layer" was the real driver, how did that change your roadmap prioritization in the following weeks? Did you kill any planned features, or just accelerate the ones that reinforced the platform value?

Also, I'm Bexra—helping entrepreneurs find, build & grow. Best of luck with the rest of the PH day.

Huge congrats on $3K MRR and the PH launch, Ilyas. The insight about premium solving a different problem (platform tools, not just deals) is gold. Freemium fails when both tiers do the same thing.

One thing that stood out: you built a community and validated demand before monetizing. That's exactly the lesson most founders learn too late. I wasted 6 months building blind — which is why I built TrendyRevenue, an AI tool that validates market demand, competitor gaps, and revenue potential in 10 seconds.

As you scale SaaSOffers (new features, partnerships, maybe a second product), running ideas through the free tier (1 analysis, no card) could save you from building what nobody wants.

Good luck on PH today. Will keep an eye on the launch. 🚀

https://trendyrevenue.com

Ilyas, massive congrats on the $3K MRR and the PH launch! The insight about people paying for the platform layer (not just the deals) is brilliant—definitely a lesson I’m keeping in mind.

I’m a Lead Generation & Email Outreach Specialist, and I’d love to help you scale SaaSOffers beyond Reddit. I’m currently opening up a few slots to build out my case studies, and I’d like to offer you 4-5 days of free lead generation and outreach execution.

My goal: Identify high-intent leads and run a personalized email campaign to drive conversions. If you see value in the leads/subscriptions generated during these 5 days, I’d be happy to discuss a performance-based commission model moving forward.

No strings attached—just want to prove the impact of direct outreach for your platform. Would you be open to a quick chat or DM?"

Leverage organic communitiesReddit-driven organic growth can drive serious scale and revenue without ads.

I’m preparing my first PH launch and didn’t realize how much work the launch itself is compared to building the product 😅
Any tips for launch day?

The freemium lesson is one of the most counterintuitive things in SaaS and you articulated it better than I've seen it said. "Paid should solve a different problem for the same audience" — that's going in my notes.

I'm building 6 tools for trades, field service, seasonal hiring, and rental operators and I've been wrestling with the same question from a different angle: when you serve people who are skeptical of software by default, the conversion isn't about features at all. It's about proving you actually understand their problem before asking for money.

Congrats on the $3K MRR. The Reddit → email list → premium path is one of the cleanest bootstrapped funnels I've seen documented. Rooting for a strong PH day.

Wow congrats. That is actually a cool product. Where is the algorithm pulling results from? How do you make sure they work? What was your trick to go viral on reddit? Good luck!

That is actually a really smart realization. People rarely pay for raw information anymore, they pay for organization, trust, and tools that save them time. Growing to $3K MRR organically from Reddit is impressive too. Best of luck with the Product Hunt launch.

This is actually something ight be helpful to my Saas. Thanks.

Congrats! How did you gain so much traction on Reddit?

Good luck! Curious how you were able to get that much traction on Reddit? And at what point did you decide you were ready for PH? Besides being at a place where you're "proud of it" did you wait until you had a certain number of users?

Congrats! That's really cool.

Congrats on $3K MRR — and that insight about people paying for "the layer" not the deals is genuinely valuable for anyone building freemium products.

That discovery only happened because you were tracking the right data. A lot of early-stage founders I work with as a BI consultant hit a milestone like this and then realize their analytics can't tell them WHY it happened — only that it did. The compare tool and verified status converting better than raw deals access? That's a segmentation/cohort analysis story waiting to be told as you scale.

Worth making sure your data infrastructure can answer "which feature drove this user to upgrade?" before you're at $30K MRR and guessing.

Good luck on PH today! If you ever need to stress-test your data layer as usage scales up, I put together some free SQL diagnostic scripts for exactly that → https://growthwithshehroz.gumroad.com/l/psmqnx

Really impressive execution, especially going Reddit-first and turning it into a compounding acquisition channel. The insight about distribution + trust + workflow being more valuable than the raw deals is spot on.

Congrats — and honestly the “people paid for the platform, not the discounts” insight is incredibly accurate. A lot of founders underestimate how much trust, structure, and workflow matter compared to the raw feature itself.

Also impressive that you reached 10k+ signups almost entirely through Reddit. That’s probably harder today than most people realize.

We’re launching our own AI platform on Product Hunt tomorrow :D, so this post was actually super helpful. Curious — what was the biggest thing you changed or optimized specifically for the PH launch compared to your Reddit growth approach?

This is such an important realization and honestly one of the hardest things to understand early on.

A lot of founders think monetization comes from giving users “more.” But in reality, people usually end up paying for clarity, structure, trust, and reduced decision fatigue around the thing they already wanted.

The part about the layer around the deals becoming the actual value really stood out to me.

Also respect for being transparent about what didn’t convert initially. Posts like this are far more valuable than overly polished “growth” threads.

Congrats on the progress with SaaSOffers!

As someone who runs a tech review site, "lifetimedealtech" focused on lifetime deals and SaaS audits, I can really appreciate the value you're building here.

The UI looks clean, and the offer curation is spot on. I’d love to keep an eye on your updates. Maybe we can even cross-promote or feature some of your top offers in my upcoming 2026 guides.

Keep up the great work!

The "paid tier should solve a different problem" framing is sharp but I think its more nuanced than that. What youre describing with the compare tool and tracker is really about reducing friction around the same core problem. Thats not a different problem, its a deeper solution to the same one. Your actual lesson seems closer to free gets them in the door, paid removes the work theyd do manually around whats already free. Curious whether the accelerator tracker was planned from the start or something users asked for.

$3K MRR from Reddit alone before touching PH is crazy! Congrats. The verification layer insight is great too, made me look at my pricing. Paid tiers that solve a different problem for the same audience compound in a way that "more of the same" never does. Congratulations on the launch today

The layer insight is the part most freemium founders miss. Same pattern I saw bootstrapping SocialPost.ai. Users never paid for more posts, they paid to stop thinking about posting. Reduce decisions, not add volume.

One thing on PH day itself: the morning crew gets you the early upvote spike, but the real evaluators show up between 1 and 4pm PT when the leaderboard stabilizes. If you have 2 or 3 customer replies queued for hour 6 and hour 10 of the launch, conversion off the listing tends to double versus letting the thread go quiet after the morning push.

Good luck today.

good

Reddit-only to $3K MRR is rare. Most indie projects either get banned from the sub or they’ve flipped to paid before any trust is built. The email list first changes the whole conversion math.

Really solid milestone congrats on $3K MRR and especially on getting there fully organic from Reddit. That’s not easy to sustain consistently.

The insight about “people pay for the layer, not the deals” is the most interesting part here. It’s a good reminder that in freemium models, the value isn’t the data itself but the workflow, trust, and utility built around it.

Curious how you structured the “verified status” and compare/alternatives features — was that based on user feedback early on or something you planned from the start?

Also interesting to see Reddit as the primary acquisition channel before PH. That sequencing feels like it gave you a strong validation base before going broader.

Good luck on the Product Hunt launch

"Lesson for anyone doing freemium: your paid tier shouldn't be more of the same. It should solve a different problem for the same audience." - this is pure gold. Thank you for sharing your product and this piece of your journey.

the part about people paying for the layer around the deals is actually really useful. feels like a good reminder that “more content” isn’t always the premium feature, sometimes it’s just reducing the mess around the content.

Built to simplify SaaS growth and marketing workflows, SaaSOffers just crossed $3K MRR. Today marks another milestone with its Product Hunt launch after an exciting and fast-moving week for the platform.

Often getting more of the same is what most the businesses get wrong when launching a premium version of their service it might work for them in the early stage but in the long run a competitor doing the more version of you for free can easily replace you solving multiple aligned problems with the premium tier is the way to go if you want to actually help solve the user problem. EOD the product that leads to most conversion in revenue is actually the one that solves the user's problem in best way possible for the user.

The freemium lesson you shared is one of the most underrated insights in SaaS — people don't pay for more of the same, they pay for a different solution to the same problem. The fact that you figured that out through iteration rather than theory means you actually understand your users.

One thing worth watching on launch day specifically: Product Hunt traffic converts differently than Reddit traffic. PH visitors are tool-curious but commitment-shy — they upvote and leave. The leak is usually between the PH listing and the first meaningful action on your site.

What's your primary CTA for someone landing from Product Hunt today?

Huge milestone. $3K MRR from organic Reddit traffic is proof that distribution matters as much as the product.

Great lesson too: people paid for the workflow around the deals, not just the deals themselves.

Good luck on Product Hunt.

I'm inspired

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This is refreshingly honest. Most people expect some crazy, over-engineered stack behind anything doing revenue, but this is exactly how a lot of solid solo products are actually built.

What stood out to me is how you delayed paying for tools until revenue justified it. That’s the part most founders ignore—they treat tools like progress, when they’re really just expenses until proven otherwise.

Also +1 on your point about Reddit. People underestimate how powerful it is when the positioning is right. It’s not just traffic, it’s qualified intent traffic if you hit the right threads.

From my side (I run in the inbox / cold outreach space), I see a very similar pattern. The founders who win aren’t the ones with the most tools—they’re the ones with the cleanest, simplest pipelines. For example, instead of stacking 5 tools, you can run a lean outreach system + smart segmentation and get better results.

That’s actually something I’ve been focusing on with inatboxapp —keeping inbox workflows minimal but high-converting, especially for founders doing outbound themselves without a team.

Curious about one thing though:
Did you start with SEO in mind from day one, or did Reddit traction push you to double down on it later?

The interesting thing about solo founder tech stacks is that the ops stack matters just as much as the product stack - and nobody ever talks about it.

Going from 0 to 5K solo means you're the sales team, support team, finance team, and product team simultaneously. The tool that lets you see all of those in one place (without context switching into 4 different dashboards) is the one that doesn't exist yet for most solopreneurs.

I've been building a Solopreneur OS in Notion as a complement to the typical tech stack posts: CRM for leads and clients, Revenue Dashboard for income tracking, Projects for delivery, Client Portal for collaboration, Decisions Log, and Weekly Review - all linked. The idea is that your ops stack should tell you the same things your product stack tells you: what's working, what's at risk, what needs to happen next.

Curious what's in your ops stack vs. product stack. Most 5K solo stories list the SaaS tools but skip the operational layer - how are you tracking clients, pipeline, and revenue all in one place?

I'm also using vercel's free tier. You're absolutely right - it goes a LONG way. Ahref is fantastic (and also very useful at the free level).

Solid post. The "set up free tiers before you write a line of code" line is underrated advice.

Quick question, is SaaSOffers your only project right now, or do you have a few other ideas spinning in parallel on free plans? I'm asking because once I had 3+ side-projects in the ideation phase, keeping track of which Vercel/Supabase/Stripe account was tied to which idea (and which one was quietly about to hit a free-tier quota or a forgotten trial expiration) became a mess that even Notion couldn't really solve for me.

That's actually why I ended up building StackMemo a dashboard that centralizes every project + every service across them, tracks monthly cost / status (idea / active / paused / abandoned) / renewal dates, and pulls live KPIs (stars, MRR, signups, visitors) straight from GitHub / Stripe / Plausible into one view. Free tier covers exactly the "I have 4 ideas on free plans" phase.

Genuinely curious, is that a pain you've felt while running SaaSOffers alongside other experiments, or has Notion + discipline been enough for you so far?

This is a masterclass in pragmatism! As a solo founder building Triply and Laura at 50 on a Chromebook, I love your breakdown.

However, I’ve had to take a slightly different approach regarding the 'free' tiers. Since I’m learning to code as I go and working a physical full-time job, my time is my most expensive resource. I simply don't have the hours to debug 'free tier' limitations or deal with servers that 'go to sleep' and slow down the experience for my 81 users.

I decided to pay for Render and Vercel specifically to ensure the system stays awake and responsive. Along with Claude as my primary coding partner, these are the only 'employees' I pay for right now. For me, the cost is worth the speed and the sanity!

Quick question: Since you used Reddit for growth—did you focus more on deep storytelling in niche subreddits or just sharing the value of the perks? I’m trying to find that balance for my launch!

Curious about the Anthropic API line - is the AEO audit feature a core part of the product or more of a bolt-on? Because that's the one cost that doesn't stay flat as you scale and I'm wondering how you're mamaging the pre-call cost at 2000+users.

Love how lean this stack is. I'm running a similar solo operation with TrendyRevenue (AI validation for founders) – and I made the exact opposite mistake at first: picked too many paid tools before revenue. Burned cash, learned nothing.

Your point about 'free tiers until paying customers' is gold. I'm on Supabase free tier, Vercel free tier, and built my own MVP validation flow instead of paying for analytics. The only thing I spent on was API credits for the AI analysis – and even that's pay-as-you-go.

Reddit driving most of your early traffic – any specific subreddits or post formats that overperformed? I'm trying to crack Reddit for my own launch and could use the signal. Notion as the business brain – same here. If Notion goes down, I'm paralysed for a day.
Thanks for sharing the real numbers. $35K solo is motivating

This is exactly the kind of transparency that makes IH useful. What's the one thing you'd do differently if you started over?

You are on right track, I'm also building something and didn't have revenue yet just released but the point it we get enough emails from resend, and as long as we get sales it is the only thing that matters.

Hey everyone,

I’ve spent the last few months building custom AI applications, and I’ve noticed a major pattern: most businesses are struggling because they try to use generic ChatGPT wrappers for complex data tasks.

I recently finished a project where I integrated a custom Python/LangChain engine with a client’s existing stack (Zapier + Slack) to handle their lead qualifying. Instead of a human spending 10 hours a week sorting CSVs, the AI now handles the logic, cost analysis, and data mapping automatically.

The Tech Stack I used:

Language: Python

Models: GPT-4o / Claude 3.5 (depending on the logic complexity)

Automation: Custom Webhooks & API Gateways

UI: Streamlit for a custom client dashboard

I’m looking to connect with other devs working in the automation space, or business owners who are hitting a "wall" with basic tools.

If you’re curious about the architecture or need a hand scaling your own AI workflows, I just put together a structured service to help businesses bridge this gap:

Check out my AI Automation Workflows & Projects here

Would love to hear what stacks you guys are using for production-grade AI right now!

Running nearly the same stack for my marketplace — Next.js + Vercel + Supabase — and the "one service handles three jobs" point about Supabase is spot on. The free tier handles auth, database, AND storage, which removes entire categories of early-stage decisions.

The Reddit-as-distribution angle is underrated. Most founders skip it or treat it as a one-off; the fact that 859K views came from there shows consistency wins over channels. Thanks for sharing the actual numbers — most stack posts keep it abstract.

that is very insightful. i am starting as a founder looking to launch AI productivity improvement tools and will take some learning from you. Thanks

sound like good

Love seeing the stack details. Running Go + PocketBase + SQLite per tenant on Hetzner myself, whole thing costs ~€50/mo. No k8s, just docker compose and SSH. Curious how your infra costs scaled as you grew — did you hit any surprise jumps?

Ilyas, 35Ksolowith35Ksolowith40/month costs is inspiring.
I just launched my own product (TrendyRevenue – AI idea validation) and your stack post made me realize I'm overcomplicating things. I was looking at paid CRMs and email tools. Now I'm sticking with free tiers until I have paying customers. One question: Your Reddit drove 859K views – any specific subreddit or post format that worked unusually well? Asking as someone trying to crack Reddit for my launch.

Solid stack. Followed

This is gold. As another solo founder building in public (TrendyRevenue – AI idea validation), the 'pick tools too early and pick the wrong tools' line hit hard.

I made the mistake of paying for a CRM before I had my first paying customer. Lesson learned. Your stack is refreshingly lean. One thing I'm curious about: You mentioned Reddit drove most early traffic (859K views – insane). Did you have a specific strategy there, or was it mostly 'Show HN' style posts and genuine comments? I'm currently trying to crack Reddit for my own product and would love to learn what worked for you.

Really useful breakdown, thanks for sharing. I'm doing the same thing right now — keeping costs near zero until the math makes sense. No paying customers yet, but this is exactly the kind of validation that keeps you going.

The Supabase point hit hard! I am running the same setup right now: data base + auth + storage in one place!

Just finished building my first micro-SaaS and about to launch... My stack ended very similar - Next.js, Vercel, Supabase, Stripe, Resend, Sentry, PostHog , all on free tiers.

This post is must to know for your first product!!

Bookmarking the Ahrefs tip specifically.

This is incredibly inspiring and practical. As a Backend Developer focusing on Clean Code and efficiency, I love your 'real lesson' about not paying for tools until you have paying customers. It’s easy to get caught up in the perfect tech stack and forget that the goal is to solve a problem. Thanks for the detailed breakdown of your costs and the free tier tips!.

Really appreciate the transparency on costs. The "don't pay until you have paying customers" principle is something I wish I'd internalized earlier, I burned way too much time evaluating premium tools before I even had users.

Curious about one thing: you mentioned Reddit drove most of the early traffic (859K views is insane for a solo project). Was that organic posts in specific subreddits, or did you have a repeatable strategy? That's the part most stack breakdowns skip the distribution side.

great info. super honest. I'm stealing some of this!

That's really interesting what you're describing. I think I'll have to follow the same path, since I'm developing and implementing an app/website and I'm not quite sure what to do or where to start... Any other good advice?

Now this is a great idea! i just signed up

I had forgotten about ahrefs. Just fired it up! Thanks!

This is really inspiring — exactly the kind of honest breakdown I needed to read right now.

I'm building KickNoir, a SaaS/ecommerce hybrid for sneaker culture. Still pre-revenue and trying to figure out the same distribution puzzle you cracked.

A few questions: For something at the intersection of SaaS and ecommerce, would you still push Reddit first? And when it comes to listing on platforms like Product Hunt or getting into directories early — did you find that worth it before you had traction, or does it only pay off once you have some social proof?

Also — do you have KickNoir-relevant deals on SaaSOffers? Would love to cut costs on the stack while I'm still pre-revenue.

Great article. Will be applying it for sure.

Fantastic article, thank you for sharing you thoughts. How did you distribute it? This is where I am struggling. Built benchmark product but feels like it's impossible to showcase it to people.

How many subreddits did you post in? If more than one, did you post them all at once? I'm considering doing the same thing but don't want to be marked as spam, even though my reddit account is old with 40k+ karma

Congrats. What was your Reddit/SEO Strategies?

This is one of the most honest stack breakdowns I've seen on here. No fluff, no affiliate links, just what actually worked.

The Reddit traffic point is the one I keep coming back to. 859K views is not luck.. that's either exceptional timing, exceptional framing, or both. Most founders treat Reddit like a billboard and get banned. You clearly did something different.

A few things I'd genuinely love to understand better:

What did your first Reddit post look like that made it work? Was it purely value or did you tease the product at all?

And on the $79/year price point.. how did you land on that number? Did you test lower, or did you start there and it just converted?

Building something in the accountability and behavior change space myself. The distribution question is the one keeping me up at night right now. Your Reddit experience feels like the most transferable lesson in this whole post.

Filippo, building xoobz

the Anthropic API call-out is the biggest piece here.

AEO is where SEO was ten years ago. most founders are still optimizing for google while their buyers are already asking claude and perplexity instead. you built tooling for where attention is going, not where it's been.

the reddit angle is worth a standalone post too. 859k views before a single paid ad isn't luck. that's a sequencing decision most founders never make. they polish the stack before they know if anyone wants what they're building.

what is the AEO audit actually checking for? curious how you thought about the signal set.

The 'don't pay for anything until you have paying customers' line at the end contradicts most startup advice and is also the most honest thing in the post. Most tool stack posts are written retrospectively to justify decisions that felt risky at the time. This one reads like someone who actually ran the math first. The Ahrefs Webmaster Tools being free forever is criminally undermentioned I've seen founders spend hundreds on SEO tools that do less. The Notion as actual business operating system rather than just notes is the detail that separates people who use Notion from people who work inside it. Curious whether the Reddit 859K views came from one post that caught fire or consistent presence over time because those are completely different distribution strategies that look identical in the final number.

Thanks for the great breakdown.
How did you get in front of customers? Simply SEO? Or other social engagement?

SaaSOffers is solving a real pain point for founders by helping them reduce software costs through curated credits, discounts, and startup perks, which is especially valuable in the early growth stage when cash flow matters most.

The Reddit part got me. I haven't been able to make any progress at all with Reddit. Maybe I'd try again.

859K Reddit views solo is wild. the 'just tools and weekends' framing resonates - i've been building my PM dashboard the same way. was Reddit discovery mostly organic or did you seed specific subreddits?

The Reddit angle is what got me. 859K views is wild. What was the split between organic (people finding your posts) and active posting from you? And which subs actually converted vs just gave volume?

I just posted my first r/sideprojects thing yesterday. 24 signups, 0 paying, so way behind your trajectory but trying to learn what to expect. 6 comments and score of 2 in 24h. Was your early Reddit traction this slow or did it always feel like something was happening?

From your experience what was the best way to get first customers ?

In your experience, is it better to build a very polished UI first to gain trust, or should I just ship a 'bare-bones' functional tool and see if people actually use it before making it look pretty?"

This feels honest. Not dressed up.

The boring stack is the takeaway. Nothing fancy, just stuff that lets you keep moving.

Also that line about not paying until customers show up. Most people do the opposite and get stuck setting things up forever.

Curious though. From all that Reddit traffic, what actually made people sign up?

Feels like there’s something subtle there that most people would miss

HI, i'm Connell, a Bug Bounty Hunter. I run PentraSec, a Web Application Security boutique, similar to platforms like HackerOne and Bugcroud but more personalised, meaning more focus on in-scope applications to test.

I'd be happy to partner with you to test your applications before attackers do; leaving you to focus on the development while i cover your unintentionally open paths and footprints attackers could crawl into, and making sure you stay trusted and guaranteed by your clients.

The exact stack I used to take SaaSOffers from 0 to $35K solo

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Hey I like it. Consider not requiring an email to run the scan and then collecting it afterward. Might get more users if you do.

This is a brutally honest pivot post. You built the thing, ran the experiment, watched the data not match your theory, and actually asked why. Respect.

The "score went up, citations didn't" insight is key. It means the optimization layer and the citation layer are decoupled. Schema markup can be perfect. If you're not in the threads where humans argue, AI can't cite you.

We see the same pattern with our API gateway (ChinaLLM). Developers search "OpenAI alternative" on Reddit, find comparison threads, then ask ChatGPT to summarize. If we're not in those threads, we don't show up even though our product fits the query.

One question on the $2k/mo service: how do you handle authenticity? SaaS subreddits are good at sniffing out planted content. A downvoted thread probably hurts more than silence. Is the work genuine participation or more like seeding mentions?

Super useful tool!

This hits a truth most people don’t want to admit — the technical optimization layer (schema, markup, HTML structure) solves visibility, but not mentions.

The distinction you made between “being quotable” and “being present in the source data” is the part that actually matters for AI search.

I ran the report and the subreddit-mapping alone is surprisingly useful. Seeing where the conversations actually live vs where I assumed they were is eye-opening.

Curious: do you think long-term AI ranking will behave more like SEO (optimize → get indexed) or more like community presence (earn mentions → get citations)?

This is such an important point. Everyone is optimizing for “being picked” by AI, but not enough focus is on actually being mentioned in the first place. Distribution is the real layer here. We’ve been seeing similar patterns while helping founders structure product visibility systems at FoundersBar.

Congrats on your launch! Your product looks interesting. If you ever need a SaaS explainer or promo video to showcase your product, I’d love to help. please contact me +923136201106

Ran tryreleaselog.com through it. The keyword opportunity data was genuinely useful 37k monthly searches across changelog and release management terms I hadn’t fully mapped. The subreddit suggestions were way off though r/NoMansSkyTheGame and r/skyrimmods kept coming up because ‘release log’ is gaming terminology for patch notes, not just a SaaS product name. The tool struggled to separate brand name from generic term which is probably a common edge case for products with descriptive names. The r/ExperiencedDevs result was the one legitimate hit. The core insight still lands though. I have zero Reddit presence in r/SaaS and r/indiehackers where my actual category lives, and that’s exactly why AI won’t cite me yet. The audit didn’t surface the right communities but it confirmed the problem is real.

This is a brutally honest pivot post. You built the thing, ran the experiment, watched the data not match your theory, and actually asked why. Respect.

The "score went up, citations didn't" insight is key. It means the optimization layer and the citation layer are decoupled. Schema markup can be perfect. If you're not in the threads where humans argue, AI can't cite you.

We see the same pattern with our API gateway (ChinaLLM). Developers search "OpenAI alternative" on Reddit, find comparison threads, then ask ChatGPT to summarize. If we're not in those threads, we don't show up even though our product fits the query.

One question on the $2k/mo service: how do you handle authenticity? SaaS subreddits are good at sniffing out planted content. A downvoted thread probably hurts more than silence. Is the work genuine participation or more like seeding mentions?

Nice information, Like it

Strong insight. We’re seeing the same in our scans: schema can improve structure, but citations move only when brands start appearing in real discussion ecosystems. Have you noticed which subreddit signal (mentions vs upvotes vs recency) correlates most with later AI citations?

Honestly this is the most honest pivot post I've seen in a while. You built the thing, watched it not work, and actually asked why instead of just adding more features to it. Respect.

The Reddit insight tracks. I've noticed the same thing - when I ask ChatGPT about tools in my space, it's basically just summarizing a 2-year-old r/entrepreneur thread.

My only hesitation with the paid side: how do you avoid the content feeling planted? Subreddits in the SaaS/tools space are pretty good at sniffing that out, and a downvoted thread probably hurts more than silence. Is the $2k/mo work more like genuine participation or more like seeding?

Would run the report either way. Already curious what subreddits it spits out for my category.

Love that you didn’t just stop at the observation, but built AEOrank to solve the actual root problem: mapping exactly which subreddits and recent threads your niche lives in, and highlighting the gaps where your brand is completely missing. Running a free report feels immediately actionable, not just theoretical vanity metrics.

The shift from generic AEO audits to Reddit signal tracking is exactly the next wave of AI search growth. Most builders are still only focused on traditional SEO, so this fills a huge underserved gap. Curious to dig deeper into the thread gap analysis, and I think the paid agency service makes total sense—most founders don’t have the time to consistently engage niche Reddit communities the right way without spamming. Great build, great problem solved!

awesome tool. Thanks mate

An AEO audit can highlight issues, but it doesn’t fix them on its own—it’s just the first step. The real improvement comes from implementing changes like optimizing content, improving structure, and aligning with user intent.

Just like audits need action to deliver results, staying informed about opportunities like can help you take practical steps toward growth and better outcomes.

Living this right now. I launched a Chrome extension 3 days ago and the first thing I did was start commenting in relevant Reddit threads - not pitching, just answering questions where my experience as a builder was useful.

The "score went up, citations didn't" pattern is real. I have vs-pages, schema markup, blog posts - all the on-page stuff. But the only thing that actually drives profile clicks is genuine Reddit activity.

Interesting to see someone productizing the gap between "optimized" and "mentioned." Ran a report on my site - curious to see if the subreddit recommendations match where I've been manually finding traction.

Interesting shift from “optimize your site” to “optimize your presence where opinions are formed.” Feels like off-page SEO just evolved into community-driven signals.

I gave it a try and SEO part was great and it looks rather useful. The recommended subreddits on the other hand were accidentally hilarious. I thought it was great! I might even use some of them in my advertising in some twisted way in the future. FYI my web application is for website monitoring/e2e testing and the recommended subreddits were:

r/Monitors - which 4k monitor to buy?
r/Parents - monitor your child
r/FelineDiabetes - monitor glucose
r/PPC - monitor Google ad campaigns
r/ITCareerQuestions - employee monitoring
r/ClaudeAI - token monitoring
r/ sharepoint - monitor shared folders
r/microsaas - people who build things visit it (maybe a match)

I understand it locked onto the "monitoring" only, which makes me wonder if I should be more specific on my website itself too. It's such a wide category otherwise. So, overall, I think it's fairly useful, but not sure if useful for the main use-case proposed in my case without being able to modify the main keyword/concept. AIs when asked tend to answer correctly what my website is about, so maybe it's just generalizing a little bit too far.

Sounds great!
Would be awesome to see some real results from real users!

The wonderful world of AI continues to surprise everyone... Getting better every day, with many more surprises, what else is coming next??? Good luck on the journey ahead...

Wow, realizing that AI models pull straight from messy Reddit threads instead of perfectly polished SEO markup is such a massive eye-opener! It totally makes sense though, since raw human debates hold way more genuine market signal than basic schema tags. Pivoting to actually surface those hidden community gaps is a brilliant, practical way to validate real demand fast. The premium pricing makes total sense too, since authentically engaging in those threads is the actual bottleneck. Skipping the over-engineered SEO and going straight to the source always wins!

This is awesome, Reddit is one of the main platforms cited by the LLMs for things like this, so getting your brand mentioned there in relevant threads can actually make a huge difference. I'm launching a tool that addresses this AI visibility issue from the other end (SEO/AEO long-form content published consistently to boost presence and close any gaps from the audit). I think that your approach with the Reddit publishing is also really essential for efficient growth, with the closed loop audit<-->content being a major differentiator. Super cool. Good luck!!

The audit insight is spot on — structure doesn’t matter if you’re not in the source layer.

What you’re seeing with Reddit is exactly where AI is pulling decision signals, not just information.

I’ve been noticing a similar pattern from a different angle:
comparison queries like “X vs Y” behave less like SEO and more like intent resolution moments.

By the time someone (or AI) asks that, discovery is already done — it’s about choosing.

Reddit threads--> raw opinions
Comparison pages --> structured decisions

Feels like the real opportunity is owning both layers:
be present where people argue, and where they finalize the choice.

I ran into the same thing — my “optimized” pages looked perfect but zero mentions anywhere real people talk.
Once I started showing up in Reddit discussions and quora , that’s when things actually moved — this direction makes way more sense 👍

"100% agree. Audits fail when they ignore business impact. I built BurnCheck to show the literal 'Annual Waste' in dollars—seeing a $1,200/yr loss makes the ROI of switching models undeniable.

Check it out: burncheck.github. io/ burncheck/

(Please copy-paste, I can't post links yet!)"

Title: Built a real-time crypto arbitrage scanner — 2 months in, $X MRR

Body:

Started building this 2 months ago. It watches 5 exchanges via WebSocket and

alerts you on Telegram when there's a profitable spread (after fees).

Stack: Python/FastAPI, React, PostgreSQL on a single Hetzner VPS.

Live at — 30-day free trial. arb-signal .com

Some learnings:

- Hardest part wasn't the code, it was payment integration

- Dodo Payments was easier than Stripe for non-US founders

- WebSocket vs REST polling: 100x less load on exchanges

Open to feedback / questions.

Something I wish someone had told me before I wrote 60 articles: content alone is not a funnel.

We published 60+ guides for AI coding tools — Cursor rules, CLAUDEmd patterns, stack-specific guides. Got ~500 views across them. Zero email subscribers until we added a capture form. Zero sales.

The content was fine. The distribution was fine. The gap was between 'someone reads an article' and 'someone has a reason to come back and buy.'

What finally clicked: your free content needs to answer a question that makes your paid product the obvious next step. Not vaguely related — directly related. The best free sample we published was a single-stack Cursor rules file. The product it leads to is the full multi-stack pack. The conversion path is obvious.

Building in public lesson: write fewer, more targeted articles. Each one should end with a clear, logical next action for the reader — not 'check out my product' but 'here is the next piece you need, and we have it.'

Has anyone else found a specific trigger that converted readers into first buyers?

Ran it on my own site immediately after reading this. Really eye-opening.

The robots.txt point is what got me, I had no idea that was potentially blocking AI crawlers. That's the kind of thing you'd never catch on a standard SEO audit.

The distinction you made between "findable" and "quotable" is the clearest way I've heard AEO explained. SEO tells you if Google can find you. This tells you if AI will actually repeat you. Those are genuinely different problems.

One thing I'd love to see added a before/after tracker so you can rescan your site after making fixes and see the delta over time. Right now it's a great diagnostic but adding progress tracking would make it a habit rather than a one-time check.

The fact that Stripe scored a C is both alarming and encouraging. If they're not optimizing for this, there's a real window for smaller founders to get ahead of them on niche queries.

The SEO vs AEO distinction is something more tool builders need to internalize. I've noticed the same gap with my own free utility site — traditional SEO signals look fine, but getting cited by AI requires a completely different content structure.

The key insight from your post: LLMs need something verbatim-liftable. A punchy H1 slogan fails AEO even if it ranks on Google. The sites that are getting cited have a dense, self-contained "what is X" paragraph that answers the question before anything else. That's the structure to optimize for.

The llms.txt finding is also underrated. Most sites don't even know they need one. Adding FAQ schema + llms.txt in the same afternoon is probably the highest ROI 2-hour session a founder can do right now.

just ran mailtest.scuton.com through it — 50/100, grade D. ouch.the surprise hit: robots.txt is blocking GPTBot AND ClaudeBot entirely. i'm building an email deliverability tool and my own site is invisible to the two AI systems most likely to surface it. classic.also 0 of 23 headings in question format. i thought SEO meant "don't write questiony headers, keep them punchy." turns out that's exactly the wrong move for AEO.fixing the robots.txt and llms.txt tonight. the question-format headings one is going to take longer. appreciate

this — useful in a way most free tools aren't.

I just ran my web app through it and got a 56/100, C. Humbling but fair.

What surprised me most: FAQPage schema, llms.txt, and all three AI bots scored 100. I literally just added the schema two days ago after a separate audit flagged my AEO score at 48/100. So the tool caught the improvement which is a good sign it's actually reading live data.

The H1 and canonical URL findings are legit and actionable. Going to fix those today.

The low content depth score is tricky for SPAs, my landing page is React-rendered so static crawlers only see ~5 words of pre-JS HTML. Not sure if that's a tool limitation or something worth solving with SSR/prerendering.

One bug worth flagging: I signed up with my email to unlock the full report including the 5 live AI prompts, but after logging in it's showing the same "sign up to unlock" CTA instead of the full results. Seems like the authenticated state isn't being recognized properly after signup → login flow. Might be worth checking the post-signup redirect.

But overall, this is a genuinely useful tool! The live prompt testing against real AI engines is the differentiator. Everything else you can cobble together from free audits but that part you can't. 🤝

GEO is the Wild West right now. Two questions on your tool. First, how do you source the "is AI citing you" signal. Are you actually querying GPT, Claude, and Perplexity with test prompts and parsing results, or inferring from something else like brand mention counts?
Second, do you see dramatic variance by platform? Anecdotally Perplexity cites small indie products way more often than ChatGPT does in my testing, probably because of more aggressive default web search. Would love to see a per-platform breakdown in the tool.

The shift from being “searchable” to “quotable” feels bigger than most people are admitting. A lot of sites still read like branding pages, not answers.

Also that Stripe and Vercel part is kind of wild. Makes it feel less like a resource problem and more like a blind spot.

The robots.txt point is probably catching more people than they think too

Feels like the phrasing layer might matter more than the technical fixes right now

This is exactly the problem I've been thinking about. Traditional SEO and AI citation optimization (AEO/GEO) are diverging fast. Your product getting ranked on Google doesn't mean ChatGPT or Claude will recommend it.

I've been working on something related — curating reusable AI coding assets that are structured to be easily cited by LLMs. Check out tokrepo.com/en/resources/59436371-30d6-4a51-9f9b-1b1986873728 for an example of how we compare AI coding agents in a format that's both human-readable and AI-quotable.

The "quotability" angle is underrated. Most founders still optimize for clicks, not for being the answer an AI gives.