Before betting on a full AI build, a proof of concept answers the cheap, important question first: can this idea actually work with our data and tools? Running one well takes a tight scope and a clear success metric. Here’s how, and how dgm helps. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)
What a POC is — and isn’t
A POC is a small, focused build whose only job is to test feasibility: can this idea work with your real data and tools? It is not a production system — it cuts corners a real deployment can’t, because its purpose is to learn, fast and cheap, not to run your business (see AI proof-of-concept development).
How to run one
- Scope it narrowly — one idea, your real data, nothing extra.
- Define one clear success metric up front (the critical step — see below).
- Time-box it — POCs should be fast.
- Build the minimum needed to test feasibility.
- Evaluate against the metric for a clean go/no-go.
Define success first
The most common way a POC disappoints is having no clear success metric — so the result is an inconclusive demo everyone reads differently. A concrete, measurable criterion set before you start turns the POC into a real decision: proven or not. This single discipline separates useful POCs from wasted ones.
POC, then pilot, then build
A POC is the first rung: POC (is it feasible?) → pilot (does it work for real users?) → implementation (roll it out). Each de-risks the next.
How dgm helps
dgm scopes and frames POCs in its $399 assessment — defining the idea, the success metric, and the data to test — so a successful POC flows into a phased build at $3,999/month without throwaway work. If you’d rather explore the platform yourself first, go straight to osFoundry; if you want to test an idea properly before committing, that’s where dgm comes in.