How long does an AI proof of concept take?

It is deliberately short and fixed-scope, typically a few weeks rather than months, because its job is to de-risk a decision quickly, not to become the project itself. The exact length depends on the complexity of the highest-risk assumption we are testing and how ready your data is, which we scope in the free technical assessment before any work starts.

Do we own the proof of concept?

Yes. You own the output, the evaluation results, and the recommendation whether or not you go on to build the production system with us. The point of the proof is to give you evidence you control, not to lock you into a build.

What if the proof of concept shows the AI will not work?

Then it has done its job. You have saved the far larger cost of a production build that would not have delivered, and you have a clear, evidenced reason why, rather than discovering it after the budget is spent. We would rather tell you that early than ship a system we cannot stand behind.

All solutions

Prove the AI works on your data before you commit to building it

The most expensive AI mistake is not choosing the wrong model, it is committing budget and a quarter of engineering time to a system before anyone has shown it will actually work on your data, in your workflow, at a cost you can live with. A demo on someone else's data proves nothing about yours. A vendor's benchmark tells you how their system does on their evaluation set, not how it will do on your documents, your edge cases, and your definition of a correct answer.

A proof of concept, done properly, removes that risk before it becomes a sunk cost. We take the single highest-risk part of your proposed system and build a working version against your real or representative data. You leave with a system you can actually try, an accuracy number measured against an evaluation set we agree up front, an honest map of where it fails, a model and infrastructure cost model, and a recommendation on whether and how to build for production. You own the output whether or not you build it with us. For teams in the UAE and the wider Gulf moving quickly on AI, this is the step that lets you move fast without betting the budget on faith.

What a proof of concept has to answer before a build is justified

A proof of concept earns its place only if it answers the questions a build cannot afford to leave open. Does the system reach an accuracy your business can actually rely on, measured against a set of real cases rather than a handful of cherry-picked ones? What does it cost per query at your expected volume, in model and infrastructure spend, so the economics are known before they are committed to? Where does it fail, and are those failures the kind you can guardrail and escalate, or the kind that quietly produce wrong answers nobody catches? Does it fit the data, the security posture, and the workflow you already run, or would production require changes nobody has scoped?

We design the proof around those questions, not around an impressive demo. The point is not to show the system at its best on a good day, it is to find its real ceiling and its real floor, so the decision to build, or not to build, is made on evidence rather than on optimism.

Fixed scope, fixed price, a decision at the end

A proof of concept that sprawls is a proof of concept that has failed at its one job, which is to de-risk a decision quickly. We run it fixed-scope and fixed-price, against the highest-risk assumption in your proposed system, so it produces a clear yes, no, or here-is-what-would-have-to-change at the end rather than an open-ended research project.

You leave the engagement with a working proof, the evaluation results, a high-level architecture recommendation, a cost model, documented failure modes, and a production roadmap if the proof clears your bar. If it does not clear the bar, you have saved the cost of a build that would not have delivered, which is a genuinely valuable outcome even though it is the one vendors rarely want to sell.

Why we lead with proof instead of promises

Most AI engagements start with a pitch and a contract and only later discover whether the thing works. We invert that, because in our experience the projects that fail are almost always the ones where nobody proved the hard part early. Leading with a proof of concept is a costly signal, a firm that will build you a small working system and tell you honestly whether the full one is worth building is a firm that is not trying to sell you a build you do not need.

This is the same discipline we apply across every engagement, assess honestly, prove on real data, build for production, and operate what we ship. The proof of concept is where that discipline is most visible, because it is the point where we are willing to tell you the answer is no.

Common questions

How long does an AI proof of concept take?: It is deliberately short and fixed-scope, typically a few weeks rather than months, because its job is to de-risk a decision quickly, not to become the project itself. The exact length depends on the complexity of the highest-risk assumption we are testing and how ready your data is, which we scope in the free technical assessment before any work starts.
Do we own the proof of concept?: Yes. You own the output, the evaluation results, and the recommendation whether or not you go on to build the production system with us. The point of the proof is to give you evidence you control, not to lock you into a build.
What if the proof of concept shows the AI will not work?: Then it has done its job. You have saved the far larger cost of a production build that would not have delivered, and you have a clear, evidenced reason why, rather than discovering it after the budget is spent. We would rather tell you that early than ship a system we cannot stand behind.

Have a project like this?

Tell us what you’re building and one of our engineers will come back with a straight technical assessment, not a sales pitch.

Request a technical assessment