A proof of concept should answer one question cheaply: will this work for us? Done badly, PoCs become endless pilots that never ship. Here’s how to run one that leads somewhere. (dgm implements osFoundry, a separate company’s platform — dgm is an independent integration partner, not osFoundry. General information, not professional advice.)
What a good PoC looks like
- Narrow scope — one specific hypothesis, not “test AI.”
- Measurable success criteria — agreed before you start.
- Real data and workflow — not a toy demo.
- A production path — defined upfront.
Avoiding pilot purgatory
“Pilot purgatory” — PoCs that never reach production — comes from vague criteria and an undefined production path. Avoid it by:
- Agreeing success criteria (e.g. “90% extraction accuracy on real invoices”);
- Time-boxing the PoC;
- Planning the production work — integration, data control, scaling — before you start.
Then a successful PoC has a clear route forward.
Use real data (safely)
A PoC on real data and a real workflow predicts production far better than a curated demo that breaks on messy data. Handle that data under DPDP-aligned controls, ideally in a self-hostable environment — realistic and compliant.
From PoC to production
If it clears the criteria, move to a properly managed project with change management. If not, you’ve learned cheaply — also a win.
How dgm helps
dgm scopes PoCs in the $399 assessment with measurable success criteria and a production path, using your real data under data-control practices. On success, implementation on osFoundry follows at $3,999/month (INR approximate; 18% GST domestic) — structured so a good PoC scales, not stalls.
General information, not professional advice.