You have a prototype: your documents, a model, useful output. What you don't have is the apparatus that makes it responsible to ship — a privacy pre-step, an evaluation built from your own failures, and a gate that stops a quiet prompt tweak from deploying a system that fabricates. That apparatus usually requires an ML engineer. Groundwork scaffolds it instead.

Groundwork's units are deployment patterns, not products — think Terraform modules for trustworthy AI deployments. groundwork init <archetype> scaffolds the whole harness for a pattern:

Every check is deterministic: same inputs, same result, every time — no LLM judge, and no API key for the checks themselves.

Quick start — 60 seconds, no API key

git clone https://github.com/nickjlamb/groundwork.git && cd groundwork
npm install && npm run build
npm run demo          # document QA: the whole loop passes, fully offline
npm run demo:break    # the system starts fabricating — watch the gate go red

✗ GROUNDING demo-savings: ungrounded number "14" — not in the provided context
✗ GROUNDING demo-unanswerable-appeals: unanswerable question — the answer did not abstain

The same loop for structured extraction:

npm run demo:extraction         # documents → fields, every check green
npm run demo:extraction:break   # a guessed date of birth, a dropped field — red, by name

✗ EXTRACTION demo-referral: SCHEMA: /referral_date must be string
✗ EXTRACTION demo-referral: FABRICATED field "date_of_birth": document does not state it (got "12 April 1988")

That red build is the product: a fabricated figure, a guessed field, or a failed abstention becomes a CI failure, not a user complaint.

Ready for your own system?

npm install -D @pharmatools/groundwork
npx groundwork init                # document QA
npx groundwork init extraction     # structured extraction

How it works

... View full README on GitHub