When you need an AI proof of concept or MVP
You have an AI idea but no proof it survives your data
Vendor demos and public benchmarks never look like production. A PoC runs on your real files — PDFs, calls, tickets, drawings, whatever you actually handle.
Off-the-shelf tools failed on your real workflow
They worked in the sales call and broke on day three. A PoC tells you whether custom is worth it, and what custom would actually cost.
Your data is messy, fragmented, or one of a kind
Custom workflows mean custom risk. Better to find the edge cases in six weeks than six months into an MVP.
Internal stakeholders need proof before they sign the MVP budget
Hard to greenlight a six-figure build off a slide. A working prototype on real data turns the conversation from "if" to "how big."
AI MVP case study: investor-ready for a pre-seed round
How AI MVP development works
Discovery call
30 minutes on your workflow, your data, and the outcome you're trying to move.
Technical review
We process a sample of your real data and send back what AI sees in it.
PoC build
Annotation, model selection, training, validation. You leave with a working prototype, accuracy numbers, and a scoped plan for what comes next.
Engagements that fit the stage you're in
Discovery call
A clear yes/no on whether the PoC is worth running, and what it would look like if it is.
Ideal for:
- A live business ready to invest in AI
- Non-technical founders or executives who need tech validation
- Teams scoping vendors and budgets before they commit
AI PoC Package
A working prototype trained on your data, validated against your real workflow.
Ideal for:
- You want AI in production but need proof it works on your data
- You need a working prototype to greenlight the MVP budget
- Off-the-shelf tools didn't fit your workflow
- You're comparing vendors and want apples-to-apples on real files
Deliverables:
- Working prototype on your data
- Accuracy and cost numbers
- Architecture diagram
- MVP scope and budget you can use with any team
Frequently asked questions
An AI proof of concept (PoC) is a small, time-boxed build that tests whether an AI approach works on your real data before you fund a full product. It answers three questions with numbers: how accurate it is on your cases, what it costs to run, and what it would take to put into production.
A PoC proves the AI can hit the accuracy you need on your data; real users don't touch it. An MVP wraps that working core in the smallest product real users can use — a UI, integrations, logins — so you can learn from actual usage or show investors. We often run both back to back: PoC first, MVP once the numbers hold.
Our AI proof of concept package is $15k–35k over 4–8 weeks. The MVP that follows is scoped from the PoC results, so the budget is based on evidence instead of guesses. For a full breakdown of where AI build money goes, see How Much Does It Cost to Build an AI Agent in 2026.
4–8 weeks. Within the first days we run a sample of your real data and show you what the AI sees in it; the rest of the time goes into building, validating and measuring the prototype against your actual workflow.
No. Messy, fragmented or one-of-a-kind data is exactly what a PoC is for — it's better to find the edge cases in six weeks than six months into an MVP. Part of the deliverable is an honest read on what data work production would need.
You get a working prototype, accuracy and cost numbers, an architecture diagram and an MVP scope and budget you can take to any team. If you build it with us, the MVP runs in two-week sprints as part of custom AI development.
Book your discovery call
30 minutes on your workflow and your data. We'll tell you whether a PoC is the right next step.