TraceBoard's engineering data — requirements, links, test results, baselines — is always deterministic and always yours. AI is an optional layer on top of that core: it can propose, draft, and flag, but nothing enters your system of record without your explicit approval. The product works fully offline with zero AI configured, and it always will.
Most ALM vendors handle AI one of two ways, and neither works for regulated teams.
Legacy tools add a chat box or an "AI assist" button as a roadmap checkbox, with no architectural line between what the model suggests and what the system treats as fact. It looks modern in a demo and becomes a liability in an audit.
Newer tools build the model into the core workflow itself — convenient, until a compliance engineer has to explain to an auditor why a traceability link exists because "the model said so." For a system of record, that's not an acceptable answer.
Requirements, links, and test data are structured facts placed there by a person — never model output treated as ground truth.
Nothing an AI feature proposes becomes part of your record until a human reviews and accepts it. No silent writes, ever.
From a single suggestion to the entire deployment, AI can be switched off with no loss of core functionality — not a degraded mode, the same product.
Self-hosted and air-gapped deployments run with zero AI, zero cloud calls, and zero telemetry. This isn't a fallback — it's the baseline.
The same four-stage pipeline governs every AI feature in TraceBoard, regardless of which module it touches. Take test case generation as an example.
Given a requirement's text and context, the model drafts candidate test cases — nothing is written to the project yet.
Drafts are checked against your schema — required fields, valid states, correct linkage — before a person ever sees them.
Every draft is shown clearly as a suggestion. Accept it, edit it, or discard it — the choice is always visible and always yours.
Once approved, the test case becomes a normal, deterministic TraceBoard object — indistinguishable in the system from one written by hand, with a full audit trail of who approved it and when.
Every item below only proposes. Nothing here writes to your project without approval.
Surfaces coverage gaps across requirements, tests, and documents, and helps you prioritize which ones actually matter.
Bring in inconsistent spreadsheets or documents as they are. AI proposes a clean, mapped structure — you approve it before anything imports.
Drafts test cases from requirements and flags existing tests that may be stale after a requirement changes.
Drafts report sections from your project data and flags inconsistencies across your document set.
Structured, typed data — never generated on the fly by a model.
Every change traces back to a specific human decision, including approved AI suggestions.
Coverage, sign-offs, and traceability reports reflect only what was approved — nothing pending, nothing inferred.
The entire product, including this list, works with zero AI configured.
TraceBoard deploys self-hosted, including fully air-gapped. AI is one optional Docker service among several — remove it, and every other module runs exactly the same.
The best way to understand the approval gate is to watch it work on your own data — a messy spreadsheet, a real requirement set, or a document you're tired of formatting by hand.