How to design, run and evaluate a meaningful AI proof of concept before you commit.
dgm is an independent osFoundry integration partner — not affiliated with osFoundry’s maker (OS LLC), and dgm has no completed client integrations yet.
A proof of concept is the sensible first step before a larger AI investment. This guide shows how to design, run and evaluate one.
Designing a PoC
Define one use case, the data it needs, and clear success criteria up front. A PoC without success criteria becomes an endless demo.
Running it
Build the smallest thing that tests the hypothesis, grounded in your real data, with a human in the loop. Keep it short and measured.
Evaluating and deciding
Compare the result against the baseline and success criteria, then decide whether to proceed, adjust or stop. osFoundry’s managed cloud pins data to the US, EU or Japan — it does not currently offer a Hong Kong managed region (its nearest managed region is Japan). To keep data in Hong Kong, the honest path is self-hosting osFoundry (BYO Cloud) inside a Hong Kong cloud region such as AWS Asia Pacific (Hong Kong) ap-east-1, Microsoft Azure East Asia (Hong Kong SAR) or Google Cloud asia-east2 (Hong Kong), or running models locally on-device.
Where dgm fits
dgm is an independent integration partner that helps Hong Kong businesses adopt osFoundry — scoping a first use case, handling the build, and connecting AI to the systems you already run. dgm is independent of osFoundry’s maker (OS LLC) and has no completed client integrations yet, so everything described here is a service offered, not a past result. If you want to scope a practical first project, dgm can help you map it out.