Prototypes & Custom Builds
The fastest way to kill a bad AI idea — and to fund a good one — is a working prototype against a single use case, with success defined up front. We build that, show you real output on your data, and only then talk about the production build.
Two ways to prove it
We time-box one use case, agree what "working" means before we start, and plan the path to production from day one. Pick where the proof matters most.
Prove the value on a workflow your own team runs — the drafting, review, or research task that eats hours today. We show it working on your real material, against a target you set.
Fixed project fee · time-boxed
Prove it in a flow your customers touch — an assistant, intake, or self-service step — with the UX, safety, and latency questions surfaced early, while they're still cheap to answer.
Fixed project fee · time-boxed
From prototype to production
A POC that dazzles in a demo and dies on the way to production helps no one. We scope the prototype to answer the real question, and we hand you an honest read on what production would take.
# Prototype — scoped per idea use_case: one, well-defined timebox: weeks, not quarters success: - metric: agreed before build - demo: real data, real output to_production: - hardening: what's still needed - cost: honest estimate verdict: go / no-go, evidenced
How it runs
A prototype is a fixed-price project with a hard edge — one use case, one deadline, one clear answer about whether to build.
Where this fits
Prototypes sit early in our process — assess, build, deploy, enable — usually right after a diagnostic has pointed to the use case worth proving. When the proof lands, the same team carries it into the full custom build; when it doesn't, you've lost weeks instead of a quarter. Either outcome is a win.
Tell us the one use case you'd most like to see working on your own data. We'll scope a time-boxed prototype and a clear definition of done.