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 how you test whether AI will actually work for a problem before committing. Done well, it answers a clear question; done badly, it wastes time. Here is how to run one.
Define the question
A good PoC tests one clear hypothesis — ‘can AI draft these responses accurately enough to save time?’ — with a measurable success criterion set in advance, not ‘let us try some AI’.
Keep it small and real
Use real data (handled per PIPEDA/Law 25), a narrow scope, and a short timeframe. Include a human reviewer so you can judge quality honestly.
Decide before you scale
At the end, compare against the success criterion and decide: scale, adjust, or stop. osFoundry’s managed cloud pins data to US, EU or Japan — it does not currently offer a Canadian managed region. For data that must stay in Canada, the honest path is self-hosting osFoundry (BYO Cloud) inside a Canadian cloud region such as AWS Canada (Montréal/Calgary), Azure (Toronto/Quebec City) or Google Cloud (Montréal), or running models locally on-device. A model-agnostic platform makes it easy to try different models in the PoC.
Where dgm fits
dgm is an independent integration partner that helps Canadian 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.