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

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.