Shipped my AI tool 3 weeks ago. 0 paying users. Here's what I'm doing wrong.

That sounds like a much better activation ladder. Opening the map shows curiosity, expanding coaching shows intent, and a second JD is a useful early retention signal. I like that you are measuring the behavior that proves value instead of just the report view. I’d be curious whether the map changes completion rates for people with one dominant gap, or mainly helps with the multi-dimensional cases.

@stop25 still with you — "build something together" is just too broad for me to answer. Give me the one-sentence version: what would we build, for whom, and what you'd bring to it. A link to what you're working on works too. Happy to look at anything concrete.

catching up on a backlog of thoughtful replies — this thread keeps giving. @speechara_ai the action I'm instrumenting this week: they open the interview map and expand coaching on at least one question — from "saw the map" to "started preparing an answer." the blunter signal is a second JD: the first report is curiosity, the second is the product earning its keep. your "customer conversation and product test in the same motion" is exactly the plan for the panic-window channels. @SkillIssue you're right that the subscription fights the shape of the need — prep is urgent, temporary, and paid at the moment of panic. your interview script became the actual cohort recruiting screen: ten people who recently paid for coaching or resume help — what they bought, when, what triggered the payment. and the one-off "prep this interview" pack goes on probation next to the subscription this month. @omri_ben_shoham "you came in measuring did-they-sign-up and shifted to did-they-interact-with-the-core-insight" — that's this thread summarized better than I could. every one of those shifts came from someone here pushing back. @Lookid87 appreciate it — genuinely early days though, and this one's worth more to me alive than sold. good luck with digimarket. @stop25 appreciate the interest — what did you have in mind specifically? for context: I'm solo + AI-assisted on the core build and not looking to split the product side, but happy to hear what you're building. @osamusakurai thank you — agreed on SEO; the long-tail "[role] interview prep" pages are the next content leg after this cohort round. (and @SpirosGagr — replied to your visibility check below; that map of citation sources would be genuinely useful, please do send it.)

Hey devinjin,

Ran a quick AI visibility check for JobHunting. Asked ChatGPT «best AI interview preparation tools in 2026» and it recommended Yoodli, Huru, Big Interview, Exponent, and Final Round AI. JobHunting doesn't appear. The tools that show up are consistently pulled from Reddit threads and comparison articles where they've built presence over time. JobHunting is invisible there right now, which is fixable but worth knowing especially since you said distribution is your blind spot. Happy to map out exactly which Reddit threads and sources the competing tools are getting cited from if that's useful.

Update for everyone who took the time here: the feedback changed the plan.

Launch Edition is live — through Aug 31 the free tier is boosted so the full flow (fit report, interview map, answer coaching) is free to run 2-3 times, not just once. When credits run out, there's no paywall wall: answer 3 quick questions by email (did the report name one insight only your resume would trigger — did a specific interviewer-perspective switch make it click — would you run another for a next interview) and we top you up manually.

Three things from this thread went straight into the product: the landing and sample now open on the interview map instead of the fit report (aryan_sinh's point), the hero shows pricing instead of hiding it (SAVYX's point), and the feedback questions on the credits email are omri's measurement system, not a satisfaction survey.

Still 0 paying users — deliberately not optimizing for that until the reports consistently pass the "specific to your resume" test. If you want to be one of the people who helps find out: jobhunting.whataicando.site

The subscription may be fighting the shape of the user's need. Job seekers have an urgent but temporary job, so a paid report, interview pack, or credits could fit better than monthly access. I would interview ten people who recently paid for coaching or resume help and ask what they bought, when they bought it, and what decision triggered payment. That reveals the real buying moment.

Reflecting early is a great move! If you ever decide to pivot or offload side-projects that aren't getting traction, it's always worth seeing what they might be worth to someone else. I actually built digimarket.inf with an AI valuation tool (/valuation) for this exact scenario. Good luck with the iteration!

The sample report is the right move because people can judge the result before they commit to signing up. I ran into something similar building DictaFlow: shipping felt productive, but distribution felt vague and uncomfortable, so I kept returning to product work. It helped to turn distribution into small tests with clear numbers, like five direct conversations or one channel tested for a full week. After three weeks, I'd care more about five job seekers finishing the sample and asking a follow-up question than about getting a stranger to pay.

A different lens on this: job seekers are one of the hardest consumer markets because they are stressed, price sensitive, and gone the moment they get hired. Look at who profits when your user succeeds, career coaches, bootcamps, university career centers, and sell the same product there. That buyer renews every semester, and one deal replaces a hundred individual sales.

Three weeks with zero paying users is almost never a product verdict when you built first and started telling people later. You still do not have a denominator, so you cannot tell demand from invisibility. The honest next step is not more posts about the tool, it is finding people already mid interview prep and asking whether the fit report tells them something they did not already know. If five real candidates say the report is generic, that is a product signal. If five say it is useful and still do not pay, that is a distribution and pricing problem. Treat those as different failures, because they need different fixes.

The actual messy middle, from someone still in it: cold distribution math is brutal enough that zero at three weeks is the expected value, not a signal.

Numbers from a small-account experiment (~100 followers): a post with an external link gets algorithmically depressed 30–50%. So ~500 impressions → ~1% CTR → 2–3 link clicks → cold conversion on a $39 product at 0.5–1% → roughly 0.02 sales per post. You’d need ~40 well-targeted posts to see one sale in expected value. Three weeks doesn’t clear that.

That’s not an argument for grinding cold posts — it’s a ceiling. Without an existing audience, “0 paying at 3 weeks” almost never separates “no one wants this” from “no one’s seen it yet.” You don’t have a denominator.

What’s actually moved the needle: finding places where someone is already describing the exact problem and responding directly — not as marketing, but as the person who built the thing for that exact pain. One live conversation in the right thread beats 50 cold posts.

The distribution wall doesn’t shrink on its own. But 0 paying at 3 weeks is not a verdict — it means you haven’t found the denominator yet.

Your demo report is the distribution asset, not the landing page - "here is where you'd get exposed on this specific job" is something people screenshot and pass around, and a signup form isn't. I'd take one real public job posting a week, run it, and post the anonymised fit report where people are already prepping (subreddits, layoff Discords, comments under hiring posts) so the artifact does the selling for you. On price, interview prep is bursty rather than ongoing, so a one-off per role may convert better than S$9/mo - people buy the week they have an interview. Also worth acting on the feedback above about the grade being easy to miss: if the score isn't the first thing on screen, the report reads as generic even when it isn't.

Thanks for sharing this so openly. Your post reminded me of Jobric — another AI product in the job-search space that recently reached $3.3K MRR.

What stood out to me wasn't that one worked and one didn't, but the order of decisions. You mentioned that you built first and planned to figure out distribution later. After reading through the comments here, it feels like you're now testing several things at once: positioning, pricing, the report itself, and distribution.

If you could go back to before building JobHunting, what would you validate first and what would you delay building until you had that signal?

Hi, I have seen your app and used it. It is very good in terms of the value iy provides but the experience is not optimal. How about alowing a user to share a url for a JD instead of opening it.
Once That is done, the scoring is not visible enough. In my case, it was a small C+ below the heading which was very easy to miss. There doesn't seem to be a very obvious path for a user to follow.
If you like, I can give a more detailed feedback on how you can improve the user experience.

I started building a personal brand before I started building. However, for a long time I didn't feel comfortable sharing what I'm building. But I eventually did. I shared the good, the bad and the ugly. I took the reader along the journey. That builds trust like nothing else.

the "tell people later" trap is real — went through the exact same thing. one thing that helped: instead of marketing the tool, I started answering the specific question my tool solves in places where people actually ask it (reddit, quora, niche forums). you become the known expert first, then the tool is just "oh I also built something for this." way less painful than cold distribution.

The no-signup sample report is probably your strongest distribution asset. I would make the first experiment narrower: pick one painful job search moment, like "I have an interview tomorrow and my resume does not obviously fit this role," then post the sample report as the proof. Your CTA can be "send me one resume plus JD and I will tell you if the report is useful," not "try another AI tool." That gets you sharper feedback and maybe the first few paid users.

I'm just releasing my first product so I'm coming from the start instead of the retrospectively so take what I say with a grain of salt. I'm a full stack software engineer with over a decade of experience like you.

My product is a paid personal finance tracker. It went live a couple days ago. My distribution week so far, my ad account got suspended within a day (new advertiser in a money category trips every fraud filter Microsoft has), the deposit is stuck there pending an appeal.

The one structural thing I did that I'd hand to any backend dev in your position, I didn't build an audience, I built a free tool that answers the questions my buyers type. A site of small single purpose calculators went up first, each answering one thing someone Googles at the exact moment the paid product becomes relevant. It does the telling people continuously, while I sleep, and it doesn't depend on me being good at marketing, which I am not. I set up google search console for SEO and have slowly been making progress getting my site showing up in google searches.

You already have the equivalent asset, it's just trapped behind one URL: the demo report. If that became a page per job title, generated from the same engine, each one would answer the question someone types the night before an interview, which is your buying moment. There's a post on the front page here right now about a golf company ranking number one for "what is a calcutta" instead of "golf tournament software". Same move, and your engine can mass produce it.

On the $9 question, price is probably not why you're at zero at three weeks. Almost nobody has arrived to see the price yet is my guess. I'd leave it alone until the demo pages bring people in, and only then ask whether the number is wrong.

This resonates a lot. I built CertMinder (landlord compliance tracking, so a very different niche) and had the exact same pattern — building felt safe, telling people felt exposed, so I told myself I'd "do marketing after launch" too.

What's actually working for me right now is showing up in existing communities where the exact pain is already being talked about (landlord/property Facebook groups, in my case) rather than trying to build an audience from zero. Slower and less scalable than a proper channel, but every conversation is with someone who already has the problem — which sounds a lot like the "be present at the moment of pain" point above, rather than announcing into a void.

Good luck with JobHunting — "shows where you'd get exposed and what each interviewer digs into" sounds like a genuinely sharper wedge than most interview prep tools I've come across.

I did the thing you actually asked for: opened the demo cold and read the David Okafor report as a stranger. Answer: it does not read generic — but the non-generic part is concentrated in one place, and it is not where the page points.

The "They want" lines are the closest to what any prep tool would say ("assess whether the candidate has actually influenced decision-makers" — fine, but interchangeable). The traps are where it stops being generic: "says 'I gave them a report and they decided'" and "talks about a reporting change rather than an operating decision" are failure modes a question bank would never name. Reading them, I could hear a real candidate walking into both. The pass bars are nearly as strong. That is your proof layer.

Two concrete things from that read: (1) your sharpest marketing asset is a trap line quoted verbatim, not a description of the method — "most FBP candidates fail this question by describing a reporting activity" is a hook a generic tool cannot copy without your data. (2) The demo defaults to the finance candidate; if most of your panic-window traffic is engineers, default to Marcus Reed so the first trap someone reads is one they recognize from their own interviews.

One honest negative: the D-grade default is brave and I would keep it, but say "we will not flatter you" out loud somewhere. A stranger's first suspicion of an AI prep tool is that everyone gets a B+.

I am on a mission of changing the Broken hiring and HR process. If you are interested in collaborating let me know.

Hello devinjin, it's interesting project you're working on! i wanna know if you have a system for determining the price of the subscription.

One distribution experiment I don’t see in the thread: turn five real, redacted reports into five indexable case pages built around the exact panic-window query, not generic “AI interview prep.” For example: “backend engineer interview risks for [role type]” with the job requirements, three specific risks the map surfaced, and what the candidate changed in their prep. Start with five manually reviewed pages, not programmatic SEO. Label company-specific interviewer behavior as an inference unless you have a source. Then measure search impression → case page → demo/map interaction → upload. If the pages get impressions but no map use, the query or proof is wrong; if people use the map but do not upload, trust/input friction is the next problem. This would make search a controlled distribution test and, more importantly, show the non-generic output before asking for a signup. It does not explain zero users by itself, but it can test both discovery and proof with the same asset.

the good news is your hook is genuinely sharp. "shows you where youd get exposed" hits a real fear that the generic-question-bank tools completely miss, so this probably isnt a product problem, its a timing-and-place problem. two things about your specific audience. job seekers are episodic and broke-adjacent, they pay at the exact moment of panic (interview in 3 days) and basically never before or after, so you have to be present at that moment, not in general. that means showing up where people prep RIGHT before an interview: cscareerquestions, interview-prep subreddits and discords, blind, layoff threads. and lead with the fear, not the feature, "paste your resume and the job post and ill show you the 5 questions youre not ready for" beats describing what it does. second, as a backend dev who admits distribution isnt natural, dont try to learn all of marketing at once. pick ONE channel where your users already gather and go embarrassingly deep for 30 days before you judge anything. 3 weeks of a broad spray tells you nothing yet. which channel are you most tempted to go deep on?

The product insight is solid — "shows where you'd get exposed and what each interviewer digs into" is genuinely different from a question bank. The problem is that nobody searching for interview help uses those words. They search "how to prepare for Google SWE interview" or "what does [company] ask in system design rounds."

The distribution that works for this product lives entirely inside that 7-day panic window after someone books an interview. They're not browsing for tools. They're googling frantically. r/cscareerquestions, specific company prep threads, LinkedIn comments on interview experience posts. Being usefully present there — before you need anything from them — is the whole game.

What does the output actually look like for a specific company + role? Showing that once, concretely, with a real JD and a real (redacted) resume, would probably outperform any amount of posting.

One operational thing that can make the “distribution problem” debuggable: define one activation event before sending more traffic. For this product I’d use “the visitor opens the interview map and can name one specific risk they’ll prepare for,” not signup.

Send each channel to the sample report with a unique UTM, record map interaction, and wait for roughly 20 qualified visits per channel before drawing a conclusion. If people activate but don’t pay, test the one-off report against monthly. If they never reach or understand the map, the proof needs work. That separates a channel problem from a product or pricing problem instead of treating zero revenue as one mystery.

The thing that jumps out from reading your thread is that your measurement system just evolved in real-time. You came in measuring "did they sign up" and you've now shifted to "did they interact with the core insight" (the interview map comparison). That's the hard part.

Most of what holds shipping back isn't the build - it's that founders measure the wrong success metric and then grind on it. You're doing the opposite. You're measuring against the actual insight you're trying to communicate, not the funnel step.

"Can a stranger load a real report and name one specific thing" is a much harder test than "did they sign up," but it's the right one. Because if they can't answer that question, no distribution channel solves it. You've just saved yourself 3 months of grinding on growth while the product still fails its own test.

The fact that you rewrote the landing page because the comments pointed this out - that's the actual skill. Not building fast, but measuring against what matters and being willing to rebuild when the measurement tells you something.

Dev founder who shipped fast and ignored distribution — hit the same wall twice. What moved the needle: watch the funnel step where visitors die (landing → demo → signup) and fix that page instead of adding features. The habit that saved me: asking my analytics questions inside my editor, not opening another dashboard tab.

the distribution gap you're describing is basically the classic dev founder trap — you validated the build, not the channel. one reframe that might help: your fit report isn't just a feature, it's content. take a real (redacted) output, post it as 'here's what a fit analysis actually looks like for [role] at [company]' on reddit or linkedin. no link, no pitch. people will reverse-engineer how you made it and come find you.

Since you asked about S$9/mo: I’d treat price as one more validation surface, not the first thing to optimize. For job hunters, the buying moment is unusually compressed: interview scheduled, anxiety spikes, outcome value is obvious, but trust is low because they’ve been burned by generic AI prep.

I’d test a one-off “prep this interview” price next to the subscription rather than only monthly. Something like “$9 for this role/interview pack, then save every report if you subscribe” may match the job-to-be-done better than “add another SaaS subscription while I’m looking.”

The metric I’d watch isn’t signups; it’s whether someone asks for a second role or says “this flagged a weakness I hadn’t thought about.” If they say “nice summary,” it’s still generic. I’d collect the exact sentence they use after reading it and put that language in the hero.

Interview prep is one of the few categories where buyers post the exact question in public: someone prepping for an onsite at a company they name, asking what they'll get grilled on. That's the telling part, and it isn't a launch post. I'd take your "where you'd get exposed" output, run it against the actual role they mention, and paste the findings as the reply with no link the first several times. I'm building Viewfy on that pattern, thread scout plus drafts you approve, so treat me as biased.

The distribution problem often gets less mysterious when you stop treating it as an announcement and start treating it as being present at the moment of pain.

For a tool like this, I would find people who just described an interview at a specific company, answer the actual question, then offer to run their resume and the job description through JobHunting. That gives you a customer conversation and a product test in the same motion.

We see a similar pattern with Speechara.Ai: the first useful outcome matters more than a signup or a view. Which user action tells you the report is genuinely useful rather than just interesting?

The "telling part" gets less mysterious once you stop framing it as announcing and start framing it as being present at the moment of pain. A few things that actually move a 0-user tool out of the messy middle:

1. Trade posts for conversations. Broadcasting to a feed of ~0 followers returns ~0. What works at this stage is 1:1: go find 20 people who THIS WEEK wrote some version of "I have an interview at [company] and I'm freaking out" — r/cscareerquestions, LinkedIn "open to work" posts, layoff threads, interview-prep Discords. Answer their actual question usefully, then offer to run their real resume + the JD through JobHunting for free and send them what it flags. Ten of those beats a thousand impressions, and you'll hear in their own words whether the output lands as useful or generic. That's distribution and product research in the same motion.

2. Your differentiation is real but currently invisible. "Shows where you'd get exposed and what each interviewer digs into" is genuinely not what a question bank does — but you're describing the category, so it reads generic. Show the magic once: take a real (redacted) resume + a real posting from a company people recognize, and post the 3 non-obvious things it flagged that no generic prep would ever surface. That single artifact travels further than any "I built a thing" announcement, because it proves the claim instead of making it.

3. On "I don't naturally think about distribution" — for an interview tool, the distribution IS the trigger event. Someone books an interview and panics; your whole job is to already be there when that happens. Figure out where people go in that 7-day pre-interview window and just be consistently useful there before you need anything from them.

For what it's worth I'm stuck on the exact same wall right now (shipped a bunch, sold close to nothing, distribution is the entire game), so half of this is me thinking out loud. The messy middle is very real — you're not doing it wrong, you're just at the part nobody writes the success-story posts about.

It's important thing I think.
I think we need to pay attention on marketing part while developing any kind of platform.
First of all, need to choose the best idea which can get the interest from other people.
And then, need to try for communicating with more people regarding your idea.
Have you ever tried to share your idea with any other people?
You need to receive feedback real time while developing.
And small important thing is SEO.
The most of people are searching with keyword when they want to find anything for something. I hope your project could be in the top place when users try to search with their request - keep going 👍

3 weeks in is still early. The fact that you’re already analyzing what’s not working instead of giving up is a great sign. Focus on talking to users, finding the real pain point, and iterating fast. The first paying customer is often the hardest—keep going! 🚀

Have you tried identifying a single group of possible users within your overall target market, and talking to them directly?

I'm in a very similar phase with my own project. I can relate to the "build first, tell people later" problem. One thing I'm realizing is that distribution probably needs to be treated as part of the product, not something that starts after launch. I'm experimenting with sharing the building process, talking to people in communities where the problem already exists, and learning what actually gets people to try the product. Still figuring it out myself, but your post definitely resonates.

The gap between a working prototype and a paying user is almost always a gap in the 'pain threshold.' You built a tool that provides insight, but you need to determine if that insight is a 'nice to have' or a 'must have' for job seekers right now. If your users are still manually applying or using free templates, they don't yet feel the pain of rejection enough to pay for your analysis.

Try this: don't look for users, look for people currently deep in the interview process. Go to platforms like LinkedIn or niche developer communities where people are actively sharing their frustration with recent interview rejections. Instead of offering the tool, offer a 'manual audit.' Tell them you'll run their resume and one job description through your tool and give them a summary of the risks for free. If they find it valuable enough to ask 'how do I do this for my next interview?', then you have a product-market fit. If they ignore the summary, the problem you're solving isn't high enough on their priority list.

The thing that stands out to me after reading this thread is how quickly everyone (including your own replies) moved to tactics — pricing, channels, funnel metrics. But the actual bottleneck you described is upstream of all of that: the report sometimes works and sometimes doesn't, and you don't know the ratio.

One thing I'd suggest before any distribution push: run your own resume against 5 different job postings at 3 different seniority levels, and score each report yourself on a 1-5 "specificity" scale. If the score swings wildly, the problem isn't marketing — it's that your core product has inconsistent output quality, and driving traffic to inconsistent quality is just burning leads. Once you can reliably hit 4+ across postings, then every channel the other comments suggested will actually convert at a rate worth measuring.

I've run into this exact issue building AI features - no matter what you tell the model, it keeps falling back on the same boilerplate language. Telling it to "not be generic" doesn't really work.

What actually helped was checking the output after it's generated, not just the prompt. We look for stock phrases and structural patterns, like the same reasoning getting reused for different people, and if we catch one, we regenerate. Not perfect, but it's caught a lot of generic output before it reached anyone.

Might be worth doing something similar here, a quick pass over your own report output for the phrases you're trying to avoid, so you catch it before a stranger does.

The self-knowledge here is the useful part: “I can ship, but I don’t naturally think about distribution.” Most people never get that honest. One pattern I’ve seen with tools like yours: the fit report only converts if the reader immediately sees themselves in it. If the first screen still reads like a generic prep tool, the substance underneath doesn’t get a chance. Try showing the report against a real, messy resume on the landing page, not behind signup, and watch where people stop reading. And your question to them is the right one, just ask it where they’ll answer, not only in the post. The messy middle is the product, not a phase you survive.

The measurement you're tracking is "0 paying users," but the measurement that matters is whether the fit report actually shows something an interviewer will probe that's specific to this person's background.

You know what makes it click - the interview map switching from the hiring manager's view to cross-team and watching the same gap get probed differently. That's the insight. But if a stranger loads your demo, can they see that insight working on their actual resume and job posting? Or are they reading your description of what it does?

Right now everyone in the comments is solving for distribution, pricing, funnel. But the real question you asked is buried: when they see the fit report, does it feel generic or does it name something concrete about how this specific interviewer would approach their experience?

That's your measurement system. Not "would they pay." Not "is the UI clean." Can they load a real fit report and name one specific thing they learned about how an interviewer would probe their background - something that wouldn't apply to someone else's resume?

Once that measurement system is working - once strangers see the actual report and can articulate the insight - distribution stops being a blocker. You'll have something visibly differentiated to distribute, and every channel works better because it's showing something people can evaluate instead of imagine.

I can't give you success advice because I'm in the same spot. I teach non-programmers to solve real problems with Claude Code (kambolab), the paid course went live this summer, and my sales count is also zero. Same self-diagnosis too: shipping feels natural, distribution feels like a chore I keep deferring.
The thing that unstuck me mentally was writing a plan for the first 10 customers specifically instead of "doing marketing." Ten customers is a set of individual people you could almost name. So my plan is boring on purpose: give five copies away for honest feedback, then spend 30 minutes a day answering questions in places where my audience already hangs out. The only number I track is real conversations per week, because at zero revenue every other metric is decoration.
One question. Have you sat next to an actual job seeker while they read their fit report? At our stage one hour of watching someone's face probably beats any launch channel. It's also the next item on my own list, so I'm partly asking to force myself to do it.

Your demo page is the distribution channel - it already sells without a signup wall. Drop it where your exact users already hang out (interview-prep subs, Blind, LinkedIn) and the answer to your real question - 'does it feel generic?' - will come faster than any pricing debate. Let a few hundred strangers run the demo before you touch the $9/mo.

The distribution problem may be real, but I'd test one other thing before trying to send more traffic: whether the subscription model matches the job-hunting job-to-be-done. Interview prep is episodic. Someone can get a genuinely useful report and still not want another monthly subscription.

I'd run a 10-person manual cohort before scaling distribution: pick one narrow role, ask each person for a resume plus an actual job posting, deliver the report, then measure three things: did they open the interview map, can they name one concrete insight that changed how they'll prepare, and would they pay again for another application/report? If most get value but balk at S$9/mo, test one-time report credits or bundles before changing the product. If they can't name a specific useful insight, pricing is a distraction and the report/map still needs work.

I'd also instrument landing -> sample -> upload -> report opened -> map used -> return within 48h. Since you said the map is what makes the differentiation click, I'd make map interaction the activation event rather than signup. Then “0 paying users” becomes a debuggable funnel instead of one number.

You asked whether the fit report reads as useful or as the same generic feedback every prep tool gives. Your landing page already answers that, and the answer is not the one you want.

Your words: "when you see the fit report, does it tell you something useful, or does it feel like the same generic feedback every prep tool gives?"

The headline promises "not a generic question bank", then the proof underneath is strengths, risks, deal-breakers, likely questions and follow-ups — the exact nouns every competitor uses. The claim is differentiation; the evidence is a category description. Worse, the sample report does not contain a report. That page is 504 characters and lists what the sample shows rather than showing it, while saying No signup required. So a visitor cannot judge the one thing you are asking strangers to judge. And S$9 appears nowhere on the site — you are asking whether the price is right, but nobody who lands has seen it.

I read the page as nine sections, mark where a visitor stops believing, and rewrite the copy for those sections. Diagnosis and replacement English copy only — no design, no code, no pricing decision made for you.

The section-by-section map and the rewritten copy come after we start. The three above are free.

No client case study to show — my before-and-after demos are my own samples and I label them that way. What I can point at is this reply: I opened your site and your sample page and named three specific things instead of saying the copy looks clean.

Paste one real fit report into the sample page, redacted, and repost the same question here — if strangers still call it generic after seeing an actual report, the problem is the report and not the page, and that is worth knowing before you touch pricing.

The interesting part is that you already have a much sharper problem than “AI interview prep.” Showing where someone’s actual experience is likely to get challenged feels substantially more useful than generic advice. I’d be curious whether people understand that distinction immediately from the fit report, or only after seeing the demo.