AI proof of concept development services

Choose a provider who will say no

Ask a prospective provider how often their proofs of concept lead to a recommendation not to proceed. Some genuinely should. A provider who has never recommended stopping may be framing tests to succeed or interpreting results generously.

Agreeing the success criteria before any testing removes much of this risk, because results are judged against a standard set in advance.

What the results report should show

Questions to ask a provider

Warning signs

• Demo on vendor examples

  Says little about performance on your data.

• Criteria set after results

  Makes almost any result look like success.

• Proof of concept bundled with a full build

  Removes your chance to stop.

• No failure examples

  Every AI system fails sometimes; hiding it is a red flag.

How the engagement runs

1. 01

   Framing session

   One question, criteria and data needs agreed.

   Output: Test plan

2. 02

   Data preparation

   Your team helps assemble and label examples.

   Output: Test set

3. 03

   Testing

   Prototype run on the test set; results measured.

   Output: Results

4. 04

   Review meeting

   Results, failures and recommendation discussed.

   Output: Decision

What you keep

Whatever the result, you should keep the test set, the prompts and settings used, and the results report. If the answer is go, these become the starting point for a pilot, whoever builds it. If the answer is stop, they document why, which is useful if the idea is raised again later or if AI capabilities change enough to justify a re-test.

Re-testing later

AI models improve and change. A task that failed a proof of concept may be worth re-testing later, using the same test set and criteria. Because the test set is kept, re-testing is quick and gives a fair comparison with the earlier result.

Help framing the question

Many AI ideas start broad: use AI for customer service, or for paperwork. A good provider helps narrow these into testable questions, choosing the part of the process where AI is most likely to help and easiest to measure. This framing session is often where the most value is created, because it turns enthusiasm into a clear experiment.

If several questions emerge, they can be ranked and tested one at a time.

Data handling during testing

Agree how test data will be shared, which AI provider will process it, whether it is anonymised and when it will be deleted. Proofs of concept use small datasets, which makes careful handling straightforward, but it still needs agreeing in writing before any data changes hands.

After a go decision

A go decision leads to a pilot: a properly built workflow with integrations, validation and review, run on live work alongside the current process. The proof of concept's test set, prompts and measurements give the pilot a strong start and a benchmark to check against, whoever builds it.