AI Proof of Concept Development

A PoC answers one question: does this AI approach work well enough on your real data to justify building a product around it? It's intentionally narrow, fast, and cheaper than a full build. An MVP (our AI MVP Development service) takes an already-validated approach and builds it into a usable product with a real interface, error handling, and enough robustness for early users. Skipping the PoC and going straight to MVP means you're validating feasibility and building the product at the same time, which is a common reason AI projects run over budget or stall.

Most PoCs run one to two weeks. That's enough time to test a shortlist of models or approaches against a sample of your real data, build a narrow working prototype of the core logic, and write up the feasibility findings. It extends toward three to four weeks when the task requires custom data labeling, fine-tuning experiments, or evaluation against a larger dataset before a fair comparison is possible.

That's a legitimate and useful outcome, not a failure of the engagement. You get the same feasibility report either way, documenting what was tested and why it didn't meet the bar, which is exactly the information you need before committing an MVP budget. Sometimes a no-go on the original approach points to a viable alternative, like a smaller task scope, a different model, or a hybrid rules-plus-AI approach, and we'll say so in the report.

Not always, and deliberately so. A PoC is optimized to answer the feasibility question as fast as possible, so we sometimes use a lighter stack, such as a notebook-driven evaluation harness or a minimal API wrapper, than what a production MVP would use. Where the PoC's core logic is reusable, we structure it so it can be lifted directly into the MVP codebase rather than rewritten from scratch.

A representative sample of the real data the AI system would operate on, whether that's documents, support tickets, transaction records, or images. It doesn't need to be your full dataset, but it needs to reflect the actual variety and messiness of production data. Testing against a small, clean, hand-picked sample tends to produce a falsely optimistic result that doesn't hold up later.

No, it's the same process described from a different entry point. The Innovation Proof of Concept line item under MVP Development for Enterprise Innovation Teams and the prototyping work under Custom AI Tools both route through this same PoC engagement; they're not separate offerings. This page describes the standalone version: a focused, fixed-scope PoC you can commission on its own, whether or not you engage us for the eventual full build.