An AI proof of concept exists to answer one question cheaply: does this approach work well enough, on your real data, to be worth building for production? A good PoC is deliberately narrow, has a single measurable success metric agreed before you start, and comes with a clear path to production so it does not end up as another demo that impresses everyone and changes nothing. This guide explains how to scope one that earns a yes-or-no answer, what it should include and roughly cost, and what to insist on so the work belongs to you.
What a proof of concept is — and is not
A proof of concept tests feasibility and value on a single, well-defined slice of a real problem. It is not a pilot, not an MVP, and not a production system. The point is to reduce the biggest unknown — usually "will the model be accurate and reliable enough on our messy data?" — before anyone commits a production budget. Treat it as an experiment with a hypothesis, not a mini-product. If you want something users actually touch, you are describing an MVP, and our note on why a working MVP beats slides covers that distinction.
Avoiding pilot purgatory
Pilot purgatory is the state where promising AI experiments accumulate but none reach production. It happens for predictable reasons: the scope was too broad to ever finish, success was never defined so nobody could declare victory, the data needed for production was never actually available, or the demo lived in a notebook with no route into real systems. The cure is built in at scoping time. Every choice below is really a defence against getting stuck.
Step one: choose one narrow, measurable use case
The single most important decision is what not to include. Resist the urge to prove a platform; prove one workflow. A strong PoC candidate has a clear input and output, a way to judge whether the output is right, and enough representative examples to test against. "Use AI to improve customer service" is not scopable. "Draft a first-response email for inbound support tickets in category X, which an agent then approves" is.
Good signs your scope is narrow enough
- You can describe the task in one sentence without the word "and".
- You can name the metric you would check to decide success.
- You have, or can get, a set of real examples with known correct answers.
- A subject-matter expert can review a sample of outputs in an afternoon.
Step two: define the success metric before you start
Agree, in writing, what "good enough" means before any code is written. The metric should be specific to the task: for an extraction job it might be the share of fields pulled correctly; for a classifier, accuracy on a held-out set your experts labelled; for a drafting assistant, the proportion of outputs an expert accepts with only minor edits. Set a threshold that maps to a business decision — the number above which production is worth funding, and below which it is not. We deliberately avoid promising a particular figure here, because an honest target depends entirely on your data and the cost of a mistake. The discipline is the same one behind measuring AI ROI: decide what you are measuring before the result can bias the goal.
Include a baseline
A metric only means something against a baseline. Measure how the task is done today — by a person, a rule, or an existing tool — so the PoC result is a comparison, not an isolated number. Sometimes the honest finding is that a simple rule already does the job and AI adds cost without benefit. That is a successful PoC: it saved you a production budget.
Step three: secure real data access early
The most common reason a PoC fails to reach production is data that was promised but never delivered, or sample data that looked nothing like the real thing. Insist on access to representative production-like data at the start, with the access, privacy, and security questions resolved up front rather than discovered later. If the data is locked in systems nobody can export from, that is itself a finding, and often the real first project is data engineering rather than the model. Testing on a clean toy dataset and then meeting reality in production is how good demos quietly die.
Step four: plan the path to production in advance
A PoC without a production plan is a dead end by design. Before starting, agree what a yes-decision triggers: roughly what production would cost, what it would integrate with, who owns it afterwards, and what would have to be hardened. You do not need the full build planned, but you need to know the demo can become a system. Production-grade AI carries concerns a PoC can skip — monitoring, evaluation harnesses, guardrails, retries, and human oversight — and our overview of the production AI stack and the realities of running AI agents in production describe what that step actually involves. Knowing this in advance stops a PoC being scoped in a way that cannot scale.
What a good PoC includes
A credible proof of concept delivers more than a working demo. Expect:
- A scoped use case and a written success metric with a baseline.
- A small, representative dataset assembled and, where needed, labelled.
- A working prototype that runs on that data — not slideware.
- An evaluation: the prototype scored against the metric, honestly, including where it fails.
- A clear recommendation — proceed, adjust, or stop — with the reasoning.
- A rough production plan and cost if the answer is proceed.
- The code, handed over to you.
The evaluation is the part that separates engineering from theatre. Anyone can show a model getting one example right; a PoC has to show how often it is right across a representative sample, and be candid about the cases it gets wrong and why.
What a PoC costs and how long it takes
Scope, not ambition, drives cost. A genuinely narrow PoC is a short, fixed-scope engagement — typically a few weeks rather than months. At Crux Digits a proof of concept starts from around €20,000, and a lighter discovery or feasibility audit from around €2,500 if you first need to work out whether a PoC is even warranted. Beware open-ended day rates with no defined deliverable; that is how budgets drift and pilot purgatory begins. A fixed scope with a defined evaluation keeps everyone honest.
What to insist on as the client
Three things protect you, and you should write them into the agreement:
- Data access on real, representative data — with privacy and security handled, so the result reflects production reality.
- A documented evaluation against the agreed metric, including failure modes — not a curated highlight reel.
- Code ownership and a handover — you keep the code, the prompts, the evaluation set, and the documentation, so you are never locked to one supplier to continue.
Vendor-neutrality matters here. A PoC should recommend the approach that fits your problem, not the one that suits the supplier's preferred platform. If the recommendation always points to the same proprietary stack regardless of the question, treat that as a warning sign.
Putting it together
The thread running through all of this is restraint. Pick one workflow, agree what success means and measure it honestly against a baseline, get real data and a production plan before you start, and keep ownership of what is built. Do that and the PoC gives you a clear, evidence-based yes or no — which is exactly what it is for. Either you fund production knowing it will work, or you save the budget knowing it would not.
If you are weighing an AI idea, our engineering-led PoCs are deliberately scoped to give you that answer: a proof of concept from around €20,000, a feasibility audit from around €2,500, and production builds from €50,000 once the case is proven. See our transparent pricing, browse case studies, or book a free consultation and we will help you scope something narrow enough to ship.
Frequently asked questions
What is the difference between an AI proof of concept and a pilot?
A proof of concept tests whether an approach is feasible and valuable on a narrow slice of a real problem, with a single success metric. A pilot is a limited but real deployment with actual users, run after a PoC has already shown the approach works. Confusing the two is a common cause of pilot purgatory, because a PoC scoped like a pilot rarely finishes.
How long should an AI proof of concept take?
A genuinely narrow PoC is usually a matter of weeks, not months, because its scope is deliberately small and fixed. If a proposed PoC stretches over many months with no defined deliverable, the scope is probably too broad. A tight scope with a written success metric is what keeps the timeline — and the budget — under control.
What should an AI proof of concept cost?
Cost follows scope. A narrow, fixed-scope PoC at Crux Digits starts from around €20,000, while a lighter feasibility audit starts from around €2,500 if you first need to decide whether a PoC is justified. Be wary of open-ended day rates with no defined deliverable, which tend to drift over budget without producing a clear decision.
Why should I insist on owning the PoC code?
Owning the code, prompts, evaluation set, and documentation means you can take the work to production with any team, rather than being locked to the supplier who built it. It also lets you re-run the evaluation yourself if your data changes. A vendor-neutral partner should hand all of this over as a normal part of the engagement.
What happens if the proof of concept fails?
A PoC that returns a clear no is a success, not a waste. It tells you, for a small cost, that the production budget should go elsewhere — or that the real first project is data engineering rather than a model. The point of a PoC is to make that decision cheap and evidence-based before larger money is committed.