What if the PoC says “no-go”?

Then the PoC did its job: you’ve spent a small, fixed amount to avoid a large one, and you have a written explanation of why. Often the report also points to a narrower version of the idea that does work.

Can the PoC code be reused?

Yes. It’s built in your cloud with production in mind, so a “go” means we extend it rather than start over.

What do you need from us?

Access to a representative data sample, 2–3 hours from the people who do the work to build the test set, and a business owner for the go / no-go decision.

Service · AI proof of concept

Know if it works before you fund it.

A proof of concept exists to answer one expensive question cheaply: does this AI idea work on your data, at a cost that makes sense? Ours is time-boxed, runs on your real data, is measured against a test set agreed up front, and ends with a written go / no-go.

Frame

Goal · pass criteria · data access

Test set

50–100 real cases

1.5

Build thin slice

End-to-end on real data

2.5

Measure & iterate

Error analysis

1.5

Report & decision

Go / no-go

0.5

035 weeks

A typical proof-of-concept shape. Scope is fixed; the answer is what you buy.

What you get at the end

Measured results

Accuracy on the test set, broken down by case type, with examples of good and bad outputs.

Failure analysis

Why it fails where it fails — and whether each failure type is fixable, and how expensive the fix is.

Cost & latency model

Cost per request and response time, projected to your production volume.

Architecture for production

What a production version would need: integrations, guardrails, permissions, operations.

Estimate for the next step

An itemised estimate for the production build, if the answer is “go”.

Everything we built

Code, prompts, test set and scripts — in your repository, yours to keep.

What makes a PoC worth paying for

A PoC that proves nothing

Success criteria defined afterwards
Clean sample data
Judged by a live demo
No cost numbers
Thrown away afterwards

A PoC that decides

Pass criteria agreed up front
Your real, messy data
Judged against a test set
Cost per request at your volume
Built to carry into production

If you’ve been shown an impressive demo and want to know whether it holds up, read 9 tricks that make an AI demo look better than the product first.

Good candidates for a proof of concept

Answering questions from your documents or tickets (RAG)
Extracting structured data from invoices, contracts or forms (document AI)
Classifying and routing incoming requests
Drafting replies or reports that people review
An agent handling a bounded back-office task (AI agents)

Frequently asked questions

GuideWhy AI pilots die before productionAI pilots stall on data, unclear value, cost and trust — rarely on the model. Seven failure patterns and the fixes that get AI into daily use.Guide9 tricks that make AI demos lieCherry-picked questions, clean sample data, hidden humans, unlimited budgets: how AI demos mislead buyers — and the questions that expose them in five minutes.GuideLLM evals for non-ML teamsHow to evaluate an LLM feature without a data-science team: build a test set, choose metrics, use LLM-as-judge carefully, run evals in CI and read the results.Free toolAI project effort estimatorFree AI project calculator: six questions → timeline, team, effort in person-weeks and the main cost drivers of your AI project. No signup.ServiceAI developmentAI systems that reach production: RAG assistants, AI agents, document AI and LLM features — in your cloud, with evals, guardrails and full ownership.

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