An AI proof of concept
that ends with a go or no-go decision.
Tested against real or representative data, with acceptance criteria defined before we build — and an honest recommendation whichever way it lands.
Four different things,
often confused as one.
| Stage | What it proves |
|---|---|
| Prototype | The idea is technically possible, on cherry-picked examples |
| Proof of concept | It works against real or representative data, with defined acceptance criteria |
| MVP | The smallest version a real user can actually depend on |
| Production | Built for scale, monitoring, and failure handling — see custom AI development |
Good questions,
and bad requests.
- "Will this actually work on our real documents/conversations/data?"
- "Is the accuracy good enough to be useful, or just impressive in a demo?"
- "What would it cost to run this at our actual volume?"
- "Can this integrate with our systems without a platform rebuild?"
- "Build something that looks impressive for a board meeting"
- "We don't have real data yet, just use something similar"
- "Skip the evaluation, we just need to see it work once"
- "We already decided to build it, this is just a formality"
What we actually
measure.
Measured against a test set you agree represents the real distribution of cases.
Projected at your actual volume, not the PoC's small sample size.
Whether it can realistically connect to the systems it needs to touch.
Whether the data-handling requirements are achievable within your constraints.
Whether the people meant to use it actually would, in their real workflow.
Whether your team can realistically run and maintain it once it's built.
What you get,
either way.
A working prototype, the measured results against your acceptance criteria, an architecture outline, a risk log, a cost model at real volume, a production backlog if it's a go, and an explicit recommendation — go or no-go, stated plainly, not hedged to avoid an uncomfortable answer.
| Dimension | Sample weight | Example threshold | Scored as |
|---|---|---|---|
| Accuracy / task success | 35% | ≥85% on the agreed test set | Measured, not estimated |
| Cost at real volume | 20% | Under an agreed per-unit ceiling at projected scale | Modeled from PoC-stage inference cost |
| Integration feasibility | 20% | No blocking dependency on infrastructure that doesn't exist yet | Confirmed against your actual systems |
| User fit | 15% | The intended users would actually adopt it in their real workflow | Checked with the people who'd use it, not just stakeholders |
| Operational ownership | 10% | Your team can realistically run and maintain it post-launch | Confirmed, not assumed |
Weights shift per project — a compliance-sensitive use case weights integration feasibility and operational ownership higher; a pure cost-savings case weights the cost line higher. We agree the weighting with you before scoring starts, not after we see the results.
What each decision
actually looks like.
Task success hit the threshold on real data, and the per-unit cost projected at full volume stayed under the ceiling. Recommendation: proceed, with the PoC's eval set and architecture outline carried into the build.
The system works well on the common case but not on a specific document type or edge case. Recommendation: ship the scoped version now, flag the excluded case as a phase-two item rather than blocking the whole launch.
Task success fell meaningfully short on real data even after tuning. Recommendation: don't proceed to build — usually because the underlying data wasn't consistent or complete enough yet, which is itself useful to know before spending on a full build.
Across the services
we build.
We run PoCs across RAG, agents, document AI, voice, and vision — the methodology is the same each time: real data, defined acceptance criteria, and an honest recommendation at the end.
Typically
3 to 6 weeks.
Milestones are agreed up front: data and criteria defined in week one, build and test through the middle weeks, and a final readout with the measured results and recommendation. This often follows an AI readiness assessment, which identifies which use case is worth testing first.
No sales pitch,
just how we work.
We're not incentivized to force a yes you'll regret funding.
Not a cherry-picked sample that flatters the result.
What it costs at 10 requests and at 10,000 are very different numbers.
If it's a go, the backlog and learnings feed directly into the build.
Before you
book a call.
What is the difference between a PoC, a prototype, an MVP, and production?
A prototype shows an idea is possible. A PoC tests it against real data with defined acceptance criteria. An MVP is the smallest version a real user can depend on. Production is built for scale and failure handling.
What if the answer is no?
Then you get a clear no-go recommendation and the reasons behind it, which is often worth more than a forced yes.
How long does a PoC take?
Typically 3 to 6 weeks depending on data availability and integration complexity, with milestones agreed up front.
What happens if the PoC succeeds?
You get a production backlog and cost model so the next phase is scoped, not a leap of faith.
How is this different from the AI readiness assessment?
The readiness assessment evaluates where AI is worth pursuing across your business. A PoC tests one specific use case against real data.
What does an actual scorecard look like?
A weighted set of dimensions — typically accuracy, cost at real volume, integration feasibility, user fit, and operational ownership — each with an explicit threshold agreed before we build, not decided after we see how it performed. The weighting shifts per project depending on what actually matters most for your use case.
Is a "no-go" really a possible outcome, or is that just marketing?
It's a real outcome, and it happens. The most common reason is the underlying data wasn't consistent or complete enough yet to clear the accuracy threshold — finding that out for a few weeks of PoC cost is the entire point, versus discovering it after a full build.
Tell us the question
you actually need answered.
Book a 30-minute call — we'll tell you honestly whether a PoC is even the right next step.