AI proof of concept
AI proof of concept: prove the value on your real data, in weeks
An AI proof of concept scoped to a single case, built on your real data, measured, ending in a clear go/no-go. For teams that want proof before committing a build budget.
What an AI PoC is, and what it is for
An AI proof of concept is a short, focused prototype that answers one question: does AI actually work on your case, with your data, well enough to justify a real project?
The point is to de-risk before you commit a build budget. In a few weeks you know whether it holds, instead of finding out after months. An honest PoC can also conclude no, and that is already an answer that saves you a failed project.
If you do not yet know which case to prioritise, an audit comes first: it lists the cases worth doing. The PoC takes one and proves it. The two work together.
What you get
At the end of a PoC you do not leave with a hunch, but with four concrete things.
- A focused use case, scoped together: one problem, a clear boundary.
- A prototype running on your real data, not a demo toy.
- Measurements: evals that quantify quality, the cases that pass and the ones that break.
- A reasoned go/no-go, with the cost and the path of a build if we continue.
On your real data, not a demo
The gap between a demo and reality is almost always the data. A sales demo runs on clean, cherry-picked examples. Your real data is messy: duplicates, missing fields, out-of-date documents, edge cases.
I run the PoC on a real subset of your data, ugly cases included, because that is exactly where an AI system succeeds or breaks. A prototype that has never seen your real cases proves nothing.
If your data is not ready, I say so early: sometimes the first step is not a model but a bit of cleanup. Better to know that in a few weeks than after a whole build.
How long and how much a sprint costs
A PoC is measured in weeks, not months. The scope is deliberately tight: one case, one dataset, one question. That is what keeps it fast and light compared with a full build.
The budget is scoped and stated before we start, on a fixed perimeter: no open-ended billing that drifts. The exact amount depends on your case and the state of your data, and we set it together on the first call. I would rather run a short, clean PoC than a fuzzy project.
The go/no-go criteria, decided together
Before I write a single line, we agree on what "it works" means for you. The final go/no-go is not an opinion: it is a reading of numbers we defined together, measured by evals, with no goalposts moving along the way.
Depending on your case, those criteria often look like this:
- A measurable quality threshold: correct answers, sourced, no made-up facts.
- An acceptable response time for real use.
- A cost per task or per document that holds up at scale.
- A sufficient level of security and access control.
From PoC to build, if it checks out
If it is a go, the build does not start from scratch: I already know your case, your data and what works. The prototype is written to be extended, not thrown away, and we add the production layer around the model: evals before every release, guardrails, observability.
Same point of contact from PoC to run, with a documented handover so your teams keep control. And if it is a no-go, you keep the measurements and a clear reason: you decide what comes next with full knowledge, with me, in-house or with someone else. The PoC stands on its own.
How long does an AI PoC take?
A few weeks in most cases. The duration depends on the scope and the state of your data: a clean case on clean data moves fast, a messy corpus needs some preparation. We set the timeline on the first call, before we start.
How much does a PoC cost?
A fixed budget, scoped and stated before we begin, on a tight perimeter. I do not quote a generic price here because it depends on your case and the state of your data. We set it together on the first call, with no open-ended billing that drifts.
What happens if it is a no-go?
You keep the evals and a clear reason for the no. It is a real answer: it stops you committing a build to a case that does not hold up. No obligation to build the next step, the PoC stands on its own.
Does my data stay with me?
Yes. Depending on your constraints, the PoC runs on an EU cloud or inside your own infrastructure, with private models if needed. The data is used only for the PoC and can be deleted at the end. Your data stays yours at every step.
Which use cases suit a PoC?
The ones with a measurable outcome: a RAG copilot over your documents, agents plugged into your tools via MCP, document extraction, process automation, anomaly detection. If success cannot be measured, it is not a good candidate yet.
PoC or audit, where do I start?
If you are weighing several cases, start with an audit: it quantifies which ones are worth it. If you already have one specific case in mind, go straight to a PoC. Either way, it starts with a free call.
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