How would you get your first customers for an AI reliability product?

I'm in a very similar spot — building a hallucination-guardrail tool for strategy/analysis reports (not support, but same "don't let the AI make things up" problem). What worked for me in early conversations wasn't pitching the product at all — it was showing one concrete example where a competitor tool fabricated a specific number/fact, then asking "would this bother you?" People self-select into the conversation from there. The "show a contradiction from their own help center" advice above is basically the same move applied to support — I'd bet it converts better than any cold outreach script.

For the first 3–5 customers, I’d avoid starting with a broad launch or paid ads.

I’d pick one narrow workflow and manually recruit 5–10 teams that already feel the pain. For an AI product, I’d ask them to connect one real use case or share a small set of representative requests, then help them measure:

• failure rate and timeout rate
• fallback behavior when a provider or model is unavailable
• latency and cost by model
• how much engineering time is spent maintaining provider-specific integrations

The important part is to define a short pilot with a concrete outcome, rather than offering an unlimited free trial. For example: “After 14 days, you’ll have a report showing reliability, cost, and the top failure modes.”

I’d also try to get the first customers from adjacent founder and developer communities, but lead with the operational problem rather than the product link. Once 2–3 teams have completed the workflow, their feedback and referrals should make the next conversations much easier.

If I were starting from zero today, my priority would be learning and a measurable customer outcome—not maximizing signups.

B2B with zero existing audience — I'd skip cold outreach and go straight to where your buyers already gather.

For customer-support AI, that's CX and support ops communities: Support Driven (Slack community, ~10K members), the Zendesk/Intercom user forums, and conferences like Support Driven Expo.

I sell to landlords. Instead of cold emailing 1,000 property owners, I joined 3 landlord associations and offered to speak at their events for free. One presentation to 50 people = more qualified conversations than months of cold outreach.

The key: the talk is about THEIR problem, not your product. The product comes up naturally in the Q&A.

For your first 3-5: offer a free 2-week pilot with a specific deliverable ("we'll audit your AI's failure modes and give you a report"). The report is valuable even if they don't buy. And the pilot gives you the case study you need for customers 6-10.

Interesting timing — we’ve been working on the adjacent problem from the enterprise side.

Have any prospective customers pushed back not on the reliability product itself, but on procurement, governance, security evidence or proving the system is safe enough to deploy?

We’ve started looking at that as a separate “deal rescue” problem: take the buyer’s actual requirements, identify what’s blocking approval and build the evidence package needed to keep the enterprise deal moving.

Curious if you’re already seeing that with Veriai.

One approach I would test for the first 3 to 5 customers is a very small, evidence-first pilot: ask for 10 real support tickets where the bot struggled, run the audit, and return a short report with the failure type, likely impact, and recommended fix. That gives the buyer a concrete result before they commit to a broad integration. We found a similar pattern with Speechara.Ai: a first useful outcome builds more trust than a general demo. For a reliability product, a before and after on one workflow may be stronger than a full feature tour. What is the smallest artifact you can deliver in the first week that a CX lead would forward internally?

It's a distribution problem more than a discovery problem for AI tools; your buyer already knows twenty options exist, so the question is why yours. I'd write up the specific failure mode your tool catches with a real before/after example and post it where people are already complaining about flaky AI agents (relevant subreddits, Discord, HN). That kind of concrete post travels further than a general build-in-public update because it solves someone's immediate problem instead of just selling a vision.

Free pilots with a hard scope worked best for the B2B products around us: pick 3 companies where you can name the exact person, offer a two-week pilot with one success metric written down before you start, and be in their Slack daily during the pilot. Cold outreach only converts when the first line proves you read their support docs or watched a call recording - generic pitches die instantly. One thing I'd do differently from zero today: hang out where CX leads complain (communities, not conferences) and just answer questions for a month before mentioning you're building anything. Trust first, demo second.

I ran veriai through a tool I built, the full read: https://kasspian.com/marketing/share/d962cab9-7087-4c74-b27c-b891eb573964

The blunt bit: every VP of CX you'll talk to has already been burned by a hallucinating bot, so the demo that catches a REAL failure is the product. it found live rooms too, the "why most support AI fails" articles have comment sections full of your exact buyers right now

Selling reliability has a trust problem the audit-first advice above solves only half of: your prospect has no way to check that YOUR checks are any good. Whatever claim you make, make it independently re-runnable. Ship the eval corpus or at least the methodology with real numbers, so a skeptical CX lead can verify you instead of believing you. A reliability vendor asking to be taken on faith is selling the disease they cure.

One data point from my own zero-customer stretch in a nearby niche (AI-automation audits): the content that got practitioners talking to me was never about my product. It was a postmortem of my own guardrail failing silently, with the measured cost. Publishing your own failures, with numbers, buys more credibility per word than any feature list - and if you do the free-audit route, each pilot can produce one anonymized finding you publish with permission. Delivery becomes your distribution.

Not in B2B AI but I went through the same "first users from zero" problem with a consumer tool last month. What actually worked was being where people already complain about the problem. For me that was Reddit threads where people were asking questions my tool answers. I didn't link it, just helped people and mentioned it when relevant.

For your case I'd probably spend a week in Zendesk/Intercom community forums and find the support leads who are actively posting about their bot giving wrong answers. Those people are already spending time trying to fix it. You don't even need to pitch, just ask them about the failure and offer to look at it.

The free audit idea others have mentioned is solid but I'd keep it stupidly small. Don't say "send us 500 conversations." Say "send me 10 tickets your bot got wrong this week." Lower barrier, faster yes.

The trust angle makes this different from most B2B tools - nobody switches reliability vendors off a cold email. What worked for me with a dev-facing tool was picking 5 teams I had a loose connection to (ex-colleagues, a Discord I was active in) and offering to sit in on one incident review instead of pitching; the product conversation came after. For customer-support AI specifically, I'd also watch the Intercom and Zendesk community forums - teams complain there in public when their bot hallucinates, and that's a warm intro.

I am not B2B AI. I am trying to get 3 real users for a free consumer product with no ads. Same problem. First people. No budget.

What failed:
A URL plus can you try this. Friends go silent. Community posts with a live link get treated as marketing. Asking for honest feedback made people protect me instead of using it.

What I would do from zero today:
Name 5 companies you already know. Ask one specific job, not a demo. For you that is probably show me 10 real support tickets, I will run them through this, then tell you what broke. A free pilot with a clear end date beats a sales call.

Do not start in a big community with the product link. Comment where those support leads already complain. Then ask 3 of them directly.

If I were you I would skip ads and skip a public launch until 3 companies have actually run tickets. The first 3 will come from people who already trust you, or from one founder who forwards you, not from a clever post.

I’m also starting from zero with a digital product, so I’m facing a similar challenge.

If I were in your position, I’d focus first on finding a small number of companies that clearly have the problem your product solves and reach out personally rather than trying to scale too early.

I think a free or low-friction pilot could work well, especially at this stage, because the main goal is probably learning what companies actually need and getting feedback.

I’d be interested to know: have you already spoken directly with potential customer-support teams about their biggest AI reliability problems?

Direct outreach with a super high level of personalization works best when starting from zero. Providing free trials or lifetime deals to early users in exchange for real feedback and testimonials creates the social proof you need before driving cold traffic. That's currently my exact focus as a front-end developer launching web templates

If I were starting from zero, I’d focus on finding 10–20 companies already using AI for customer support and talk to the people responsible for those systems. Instead of leading with the product, I’d first try to understand where reliability issues are costing them time or money. A small free pilot with clear success metrics could then be a good way to turn those conversations into the first few customers. Founder-led outreach would probably be my starting point before experimenting with communities or paid acquisition.

Something I learned building AnchorStrategy that ties into what's already been said here: reliability claims don't land in words, only in a demo the prospect can watch happen. I ended up building a landing page where visitors can watch, in real time, the moment my tool refuses to fabricate a number instead of guessing — no signup, no pitch, just the failure mode not happening. That single moment does more convincing than any amount of "we're reliable" copy.

For your customer-support AI specifically, the equivalent might be: pull a handful of real (anonymized) tickets a prospect has already gotten wrong answers on, and show them, live, your system catching what theirs missed. Same principle as the audit idea above, but the delivery is "watch it happen" rather than "here's a report."

I’d start with 3–5 tightly scoped pilots rather than broad outreach. For an AI reliability product, the pitch is much stronger if you can show one concrete outcome, like fewer hallucinations, lower escalation rates, or better response consistency.

One thing I’ve noticed from companies like GeekyAnts with their AI accelerators is the value of leading with a specific workflow and measurable problem instead of selling “AI” in general. I’d use that same approach here: pick one customer-support use case, prove reliability improvement, then use those early results to open the next few conversations.

Founder-led outreach plus niche communities would probably be my first channels.

For a reliability product you have an unfair advantage: you can demonstrate the problem before you ask for anything. Don't cold-pitch "we improve AI reliability." Find companies with a public support AI, use it, catch it failing. Then outreach isn't a pitch, it's evidence: "I asked your support bot X and it told customers Y, which is wrong. Found four more. Want the list?"

You're not claiming they have a problem, you're showing one live and customer-facing now. Fear of a visible failure moves B2B buyers faster than any feature list. The free audit IS the sales motion.

Pick 10, spend an hour breaking each, lead with the failure you found. Five evidence-backed messages beat 500 generic ones.

What does it catch best, factual errors, policy violations, or tone misses? Lead with that.

For a reliability layer on support AI, the thing that unlocked the first few pilots for us was not pitching the product at all — it was offering a free audit of their existing bot. Ask for 200-500 anonymized transcripts, run your checks, and hand back a short doc: here are the 12 answers that were confidently wrong, here's the hallucination rate on refund/policy questions, here's what it likely cost in escalations. Most support leads have never seen their error rate quantified, so the audit itself is the value and the product becomes the obvious way to keep watching.

Two practical notes. First, target companies where a wrong answer has a money consequence — ecommerce refunds, billing, insurance, travel changes — not companies where support is mostly "where is my order." The pain has to be measurable or you're selling insurance nobody prices. Second, go to the person who owns the escalation queue (head of support / CX ops), not the ML team. The ML team thinks reliability is their job; support ops is the one getting yelled at when the bot lies.

If I were starting from zero today, I'd skip cold email volume and do 20 very specific ones referencing something I actually observed in their public-facing bot or help center, and I'd charge a small amount for the pilot from day one. Free pilots got us usage but no urgency; a $500 paid pilot got us a champion who actually scheduled the review call.

If I were starting from zero, I’d probably avoid broad outreach and focus on 10–20 companies where the pain is already visible.

For an AI reliability product, I’d offer a very narrow pilot around one measurable failure mode, for example hallucinations, escalation errors, or incorrect support actions. Then use the pilot to learn what teams actually care about enough to pay for.

I’d also spend time in founder and support communities, not to pitch, but to understand how reliability problems are described in the real world. That language usually becomes much better outreach than generic “AI reliability” messaging.

For the first 3–5 users, I’d optimize for learning and proof, not scale.

Cold outreach to people already complaining about their current tool. We found public complaints (reviews, HN, communities) are a ready-made buyer list — email them with a specific fix, not a pitch. Free pilots with a tight scope worked better than open-ended trials.

One segmentation detail I’d add: separate companies by how painful a false answer is, not just by whether they use support AI.

For the first 3–5 customers, I’d look for teams where the support bot touches refunds, billing, medical/financial policy, account access, or anything that can create a legal/compliance headache. Those buyers already understand that “mostly correct” is not good enough. A generic SaaS chatbot team may agree reliability matters, but a fintech support lead with one bad refund/escalation incident has urgency.

I’d also make the first offer painfully concrete: “send us 50 anonymized conversations and we’ll return the top 5 failure modes, severity, and one policy/test change for each.” That gives them a useful artifact even before they trust your product. If they won’t share 50 sanitized examples or schedule a review of the findings, they probably are not an early buyer yet.

So my zero-to-first-customers path would be: pick one high-consequence vertical, publish a tiny failure taxonomy for that vertical, then use it as the basis for founder-led audits. The taxonomy is what makes the outreach feel like expertise instead of another AI tool pitch.

The “first 3–5” question is the hardest part of any B2B AI tool, and I think free pilots only work if you can point at one painful failure first. For a reliability product, I’d pick 5 companies whose support AI visibly messes up in public—bad responses in their help docs, complaints on Twitter—and reach out with a single specific example of what went wrong and how your tool would catch it.

That turns cold outreach into a diagnostic, not a pitch. Then the pilot becomes an easy yes.

One thing I’d do differently from zero: skip broad communities and instead lurk in niche LLM ops Discord servers where engineers already complain about this exact problem.

What’s one concrete failure pattern you’ve seen in customer-support AI that you think founders would immediately recognize?

If I were starting from zero, I’d probably focus on a small number of highly targeted companies rather than reaching out to everyone. I’d offer a short free pilot, learn from their feedback, and use the first successful case as proof when approaching the next few customers. Founder networks could also help make those first conversations warmer.

The opener that worked least for me was 'we improve AI support quality.' The one that worked: find one publicly visible answer from their help center that two pages contradict, paste both lines, and ask whether the team would count that as a successful resolution. It needs no integration and no data access, and it gives the owner something they can verify in ninety seconds. From there, offer the three-failure audit only after they confirm that contradiction was a real incident class. First-customer motion is not a channel problem; it is a proof-of-evidence problem, so give them a finding before you ask for a pilot.

I think the “proof before the pitch” point is really important. For a local service business, I’ve found that simply telling someone what you offer isn’t nearly as effective as showing that you understand the problem they’re already dealing with.

I’d probably use the same approach here: identify a specific pain point first, give something useful that helps them see the problem more clearly, and only then introduce the product as a possible solution.

It also seems like a good way to avoid wasting time on people who don’t actually have the problem you’re solving.

The opener that worked least for me was 'we improve AI support quality.' The one that worked: find one publicly visible answer from their help center that two pages contradict, paste both lines, and ask whether the team would count that as a successful resolution. It needs no integration and no data access, and it gives the owner something they can verify in ninety seconds. From there, offer the three-failure audit only after they confirm that contradiction was a real incident class. First-customer motion is not a channel problem; it is a proof-of-evidence problem, so give them a finding before you ask for a pilot.

jumping in on stop25's unanswered question since nobody's given the practical mechanics yet: LinkedIn/Google search operators work surprisingly well for this. site:linkedin.com "hiring" "AI QA" or "AI support" narrows to companies actively building out that function right now. Google News search for "[industry] AI support agent launch" catches recent rollouts before they're common knowledge. G2/Capterra review sections for AI support tools are also underrated, unhappy reviewers are literally describing the exact failure modes you'd want to fix, with company context attached half the time

combine that with AtlasHQ's audit-as-opener idea above (still the sharpest thing in this thread) and you get a genuinely cold-outreach-free motion: search for the trigger, then open with a finding instead of a pitch

I’d start with a very narrow free pilot: review a fixed sample of anonymized support conversations, categorize the failures, and rank them by customer impact. Before running it, ask the support team to name the one type of failure they consider unacceptable. That gives you a measurable before-and-after result instead of a vague “AI quality” promise. I’d approach teams that have already publicly announced an AI-support rollout—they have the problem now and are easier to qualify.

This is a real problem, especially now that more companies are putting AI directly in front of customers. I’d skip ads and approach a small number of support teams with a free pilot using their real conversation logs, then show them exactly where the AI fails and what that failure could cost. If the results are useful, those first users can become both customers and strong case studies.

For AI tools, communities > ads at the start. We're launching on Product Hunt in 24h and the only thing that moved the needle was building a warm list of founders who actually care. IH, Twitter #buildinpublic, and Quora answers brought more qualified eyeballs than any paid channel. Start with people who feel the pain.

I’d start with a small, opinionated diagnostic rather than a broad free pilot. Pick one visible failure mode — for example, unsafe escalation or inconsistent policy answers — and offer to review a limited sample of conversations.

The important part is giving them an artifact they can share internally: what failed, why it matters, and one practical next step. That makes the first conversation useful even if they never become a customer, and it helps you learn which pain language gets a real response.

For the first few companies, I’d prioritize teams that have recently launched an AI support agent or are hiring around AI quality. The timing is probably more valuable than the size of the company.

I’d probably avoid choosing a channel first and instead look for companies showing a strong “need” signal.

For example, companies that have recently launched an AI support agent, are hiring for AI QA/support roles, or are publicly dealing with hallucinations, escalation, or manual conversation reviews would be much stronger prospects than a generic list of “Heads of Support.”

For the first 3–5 customers, I’d approach those companies with a very narrow pilot tied to one measurable outcome — finding failures their current QA missed, reducing manual review time, or preventing a specific type of incident.

Once you know which trigger consistently leads to a successful pilot, then I’d think about scaling the acquisition channel around that signal.

Adding one angle to the failure-first advice above: consider starting where reliability is not a nice-to-have but an audit requirement. We build AI systems for pharma quality teams, and what actually opens doors is not the tool — it is the evidence artifact. A compliance-shaped report (what was tested, what failed, what changed since) is something a QA lead can forward upward, and internal forwarding is how B2B products really spread. For support-AI reliability specifically: fintech, healthcare, and insurance support teams already have regulators and internal audit breathing on them — they budget for evidence, not dashboards. Second thing I would do from zero: publish your failure taxonomy publicly. Every vendor claims their AI is reliable; the one who documents exactly how support AIs fail, with real anonymized examples, becomes the reference buyers cite internally. That earns inbound without a single ad.

I would not start by choosing a channel. I would start by choosing one failure your buyer has already experienced.

Find support teams that recently deployed an AI agent and had a concrete incident: a wrong refund, a hallucinated policy, an unsafe escalation, or a set of conversations that had to be manually reviewed. Ask them to reconstruct the last incident: what happened, how they noticed it, who investigated it, what it cost, and what they changed afterward.

Then offer a narrow pilot using their own conversation logs. Manually produce the reliability report if necessary. Agree on one success criterion before the pilot, such as finding failures their current QA missed, reducing review time, or preventing a repeated incident. A free pilot is useful only if they contribute real data, staff time, and a scheduled review. Otherwise it mostly measures curiosity.

For the first 3 to 5 companies, I would search for evidence of the trigger rather than broad job titles. Posts about a recent support AI failure, hiring for AI QA, complaints about manual transcript review, and teams publicly launching an AI support agent are better prospect lists than "heads of support."

What exact reliability failure does the product detect today? That determines which recent incidents to look for.

The interesting part is that reliability is tied to a specific buyer context rather than AI quality in general. Customer-support AI gives you a concrete failure surface and a clear business consequence when reliability breaks.