AI PoC Development Company: Prove It Works Before You Fund the Build

• Enterprises needing evidence before a capital approval cycle
• Innovation and digital transformation teams with a shortlist of candidate use cases
• Startups validating that a technical assumption holds before raising or building
• Teams comparing AI vendors and wanting an independent benchmark
• CIOs who have been pitched an AI platform and want the claims tested on their own data

Everything — code, prompts, evaluation datasets and documentation — is yours regardless of outcome, and usable by any vendor you choose afterwards.

Define numeric success criteria before starting. "See if AI can help with support tickets" is not a criterion. "Correctly categorise 85% of tickets into our existing 12 categories, measured on 500 held-out tickets" is. Without a number agreed in advance, every result becomes negotiable and the PoC concludes with everyone reading their own preference into it.

Use your real data, including the messy parts. Clean curated samples make every approach look viable. Real data has missing fields, inconsistent formats, edge cases and the fifteen-year-old records nobody wants to discuss. A PoC on sanitised data tells you nothing about production.

Establish the baseline first. How accurate is your current process — the rules engine, the human team, the existing vendor? An AI system at 82% accuracy is excellent against a 60% baseline and a downgrade against 95%. Surprisingly many PoCs skip this and are unable to interpret their own result.

Data readiness usually decides the outcome. For machine learning, whether enough labelled examples exist. For RAG, whether the documents contain the answers at all. For computer vision, whether images are consistent enough. We assess this in the first few days, and if the data is not there we tell you immediately rather than spending your budget confirming it slowly.

A no is a valuable result. A PoC that concludes the use case is not viable has saved you a build. We say this plainly because the incentive in this industry runs the other way, and vendors whose PoCs always succeed are not testing anything.

Keep it time-boxed. Four to six weeks. PoCs that run longer stop being experiments and become unmanaged projects, and the cost of learning rises past the value of the answer.

Fixed price, fixed timebox, defined deliverables. No open-ended discovery.

Indicative cost: a focused AI PoC runs $8,000–$20,000 depending on data complexity and the number of approaches benchmarked. A desk-based feasibility assessment without a build typically runs $3,000–$6,000 and takes one to two weeks. Multi-use-case PoC programmes are priced per use case with a shared setup cost.

If the PoC proceeds to a production build with us, we credit a meaningful portion of the PoC fee against it — but the PoC stands alone and its outputs are yours either way.

• Numeric success criteria agreed before work starts, so the result cannot be spun
• Real data from day one, including the parts nobody wants to show a vendor
• Baseline measurement, so you can interpret whether the result is good
• We deliver no-go recommendations, and have done — a vendor whose PoCs always pass is not testing
• Fixed price and a hard six-week ceiling
• Everything handed over and usable by any vendor you pick afterwards

Bring us the use case you keep discussing without deciding on. We will tell you within a call whether a PoC is the right next step, or whether a shorter feasibility assessment would answer it more cheaply.