What is a proof of concept (POC)?
A proof of concept (POC) is a short, limited test of whether an AI approach can do a defined task well enough, on the company’s real data, to be worth building properly. A sound POC has one use case, a fixed data sample, a success threshold agreed in advance, a time box of a few weeks, and a final decision to proceed, change course, or stop.
A POC sits between model prototyping, where data scientists explore whether the data carries any signal, and a first deployment, where real users work with the system on live cases. It usually runs on historical records with known answers, so results can be scored against ground truth instead of judged by impression.
Why a proof of concept matters
A POC is the cheapest point at which to learn that a use case will not work. Problems surface quickly once real data is involved. Typical ones are scanned pages the model cannot read, fields filled in after the fact, and unusual cases that make up much of the volume. A POC also sets a measured baseline for later versions to beat.
Many proofs of concept succeed and then stall, often because they ran on a laptop with a hand-picked sample, outside security review, and with no plan for integration or support.
Proof of concept in practice
A mid-sized law firm tests whether a language model can find assignment and change-of-control clauses in vendor contracts during due diligence. The POC uses 150 contracts that associates have already reviewed, so the correct answers are known. The agreed threshold is that the model finds at least as many flagged clauses as a first-year associate and cites the page for each. The test runs on a server inside the firm’s network because the contracts are privileged. The model meets the threshold on typed contracts but misses clauses in scanned exhibits, so the firm proceeds with optical character recognition added to the plan.
Kirk Tech Solutions typically runs a POC inside the client’s own environment, under the identity and logging controls production will use, so a successful result does not need rebuilding.
What to watch for
- Write down the success criteria, and the decision each outcome triggers, before any work begins.
- Test on representative data, including poor scans and unusual cases, rather than the cleanest sample available.
- Ask a vendor whether the POC runs on the same model, infrastructure, and security setup that production would use.
- Include the eventual users in the evaluation, because their view of usefulness matters as much as accuracy.