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

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.

Both answers above are right, but they skip the thing that makes reliability specifically hard to sell cold: nobody buys reliability before they have been burned by its absence. It is a post-incident purchase. So your first-customer motion probably is not outreach at all, it is measurement. You cannot sell an invisible problem, and right now a support team's hallucination and wrong-answer rate is invisible to them, they only see the angry tickets after the fact, never the rate. The wedge with the least selling in it: offer to measure their support AI's actual failure rate on their own real tickets, for free, and hand them the number. That does three things a pitch cannot. One, it converts the abstract word "reliability" into a dollar figure they feel: percent wrong answers times ticket volume times escalation and refund cost. Two, it self-selects your first three to five companies for you, the ones whose audit comes back ugly are exactly the ones with enough pain to pay, and the ones whose numbers are clean disqualify themselves for free so you never waste a pilot on them. Three, an audit result is a far warmer opener than any cold email, because you are not asking for a meeting, you are delivering a finding. The whole thing hinges on one constraint though: can you produce that failure-rate number WITHOUT the prospect integrating anything first? If measuring them needs a setup sprint, the wedge is gone and you are back to selling a pilot. If you can get even a rough read from a sample of their real tickets with zero integration, that read-only audit basically IS your cold outreach. So which is it for you, can you measure a prospect's current reliability from the outside before they have committed to anything?

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.