How AI-native development agencies find companies worth selling to.
Your customer wants to ship something with AI in it and can't hire the people to do it. That gap is public. Long-open AI roles, 'exploring' language on the careers page, a legacy stack that can't host what they want, an RFP-shaped blog post. This profile is about finding the gap, not the enthusiasm.
Prospecting for AI-native development agencies, the way we do it: infer your ideal customer profile from your own site, pull candidates from the sources where your buyers leave signals, screen them cheaply, then read each survivor’s pages against a fixed set of checks. You get 8–25 companies, ranked, with a quote and a URL behind every claim. Try it on find-customer for free.
What your ICP usually looks like
- How it's defined
- gap-defined: companies with a stated AI ambition and visible inability to build it in-house; restate as roles ('open AI engineer roles') for the best fit rate
- Size band
- 50–2,000 employees; below that they'll use a no-code tool, above it they'll hire a team
- Likely buyers
- CTO or VP Engineering · Head of Product · an innovation or digital lead in non-tech companies
How we infer it from your site
We read your case studies for the shape of the work (greenfield product versus integration into an existing system), the stacks named in them, the client industries and sizes, and whether you sell fixed-scope builds or embedded teams. An agency whose case studies are all 'integrated an LLM into an existing platform' gets a different ICP from one whose case studies are all new products.
Typical sellers on this profile: LLM app development, AI product studios, MVP builders with AI, ML engineering partners, AI integration and platform work. You see the inferred fields before any research starts and can correct them.
Where your buyers leave signals
| Signal | Where we read it | What it proves |
|---|---|---|
| Unfilled AI roles | Public ATS boards: 'AI engineer', 'ML engineer', 'LLM', posted 60+ days ago | A long-open role is a company that wants the outcome and can't staff it |
| Stated AI ambition | News posts, annual letters, 'AI at [company]' pages, founder interviews | Sponsorship and budget, quotable, dated |
| Legacy-stack tells | Job posts naming old frameworks, on-prem systems, or 'modernisation' | They can't host what they want to build; that's the project |
| Tech adoption | Job posts and integration pages naming the systems an AI feature would sit on | You need the data source to exist |
| Lookalikes | Exa findSimilar seeded with your client domains | Companies that resemble ones you've already shipped for |
Typical fit rate: 25–40% when defined by ambition plus gap; 40–60% when restated as open AI roles. Sources whose membership already proves something beat generic search, which has a 5–10% prior; the reasoning is in how to build a target account list from your website.
Queries that find them
- site:jobs.lever.co "machine learning engineer" "first"
- "looking for a partner" "AI" "build" site:*.com/blog 2026
- "modernisation" OR "modernization" careers "legacy" "AI"
- "exploring" "generative AI" "our customers" site:*.com/news
What problem evidence looks like
A check passes only with a quote from a page we fetched. For AI-native development agencies, the lines that pass most often look like these:
- “We're looking for our first ML engineer to help us figure out where AI fits in the product”
- “Modernise our legacy .NET platform and lay the groundwork for AI features”
- “We've been exploring generative AI for document review but haven't shipped anything yet”
Default for this profile: no. If a company already has a development partner named on its site, the project is usually spoken for. Default no; flip it if you specialise in rescuing stalled AI projects.
Disqualifiers we apply
- Companies with an AI or ML team of five or more (they'll build it)
- AI vendors and other agencies
- Pre-seed with no product yet (no system to integrate into, no budget)
- Already a client (checked automatically)
What a qualified card looks like
Illustrative example, not a real company
800-person regional insurer with an ML Engineer role open for 94 days, a news post naming 'AI-assisted claims review' as a 2026 initiative, and a job post describing a legacy .NET claims platform.
- “800 employees” — /about
- ML Engineer role posted 2026-06-10, still open — /careers/ml-engineer
- “AI-assisted claims review” listed as a 2026 initiative — /news/2026-initiatives
- “legacy .NET claims platform” — /careers/senior-engineer
- Development partner — Not found
An AI role unfilled for three months plus a stated initiative
CTO
The score is arithmetic on check verdicts, not a model’s opinion; the weights are in our ICP scoring formula.
Questions AI dev agencies ask
- Can it find companies with AI roles that have been open a long time?
- Yes. We read posting dates from public ATS boards and flag roles open beyond a threshold, with the posting URL as evidence.
- We do both AI products and normal software. Should we run two ICPs?
- Probably. We infer up to two segments from your case studies; run the AI segment first, then the other. Blending them dilutes the queries.
- How do you know a company isn't already working with another agency?
- We look for a named partner on the site and in press; if there's nothing, the card says Not found rather than assuming either way.