Before betting on a full AI build, smart businesses ask a cheaper question first: can this idea actually work with our data and tools? An AI proof of concept (POC) answers exactly that — fast, focused, and low-cost. This page explains what a POC is, when to run one, and how dgm delivers it. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)

What a POC is — and isn’t

A proof of concept is a small, deliberately narrow build whose only job is to test feasibility. It is:

  • Focused — one idea, one clear question, your real data and tools.
  • Fast and low-cost — designed to learn quickly, not to ship.
  • Decisive — it produces a go/no-go, not a maybe.

It is not a production system. A POC cuts corners that a real deployment can’t — limited scope, minimal hardening — because its purpose is to learn whether the thing is feasible, not to run your business. Confusing the two leads to either over-investing in a test or under-building the real thing.

Why a POC de-risks the investment

Full AI builds fail expensively when feasibility was assumed rather than tested. A POC moves that risk forward, where it’s cheap to absorb. In a few focused weeks it can reveal:

  • Whether your data is sufficient for the idea to work at all.
  • Whether the approach is technically feasible with current tools and models.
  • What the real challenges are — the ones you’d otherwise discover mid-build.

That turns a large, uncertain bet into a small, informed one. If the POC succeeds, you proceed with evidence; if it fails, you’ve saved the cost of a full build that wouldn’t have worked.

Define success before you start

The most common way a POC disappoints is having no clear success metric, so the result is an inconclusive demo everyone interprets differently. dgm defines the success criterion up front — a concrete, measurable bar that says whether the concept is proven. That’s what makes the outcome an actual decision rather than a debate.

POC, then pilot, then production

A POC is the first rung of a sensible ladder:

  1. POC — is it feasible? (this page)
  2. Pilot — does it work for real users at limited scale?
  3. Implementation — roll it out and run it.

Each step de-risks the next, so you’re never betting everything on an unproven idea.

What dgm delivers, and what it costs

dgm keeps it simple and public:

  • Assessment + roadmap ($399, one-time). We scope the POC, define the success metric, and confirm the data and approach worth testing.
  • Build toward production ($3,999/month). A successful POC flows into a phased build and integration within the standard engagement, with no per-seat fees.

We use osFoundry because its model-agnostic, integration-first design makes focused POCs quick to stand up — and easy to carry forward if they succeed.

How dgm helps

dgm builds focused AI proofs of concept for US businesses — fast, low-cost feasibility tests with a clear success metric — so your bigger investment rests on evidence, not hope. If you’d rather explore the platform yourself first, you can go straight to osFoundry; if you want to test an AI idea properly before committing, that’s where dgm comes in.