Bring the evidence together
Requirements, decisions and current behaviour.
Requirements Assurance
Know what changed, why it changed and how it will be tested.
A source-agnostic assurance layer for requirements, delivery knowledge and test creation. It connects proposed work to the relevant decisions, code, production evidence and test cases—without requiring your team to replace the tools it already uses.
Works with your current toolsJira, Azure DevOps, Linear, ClickUp, documents, transcripts and other approved sources.
Test cases with provenanceEach scenario traces back to the requirement, risk or behaviour it verifies.
Independent change recordEvidence can be reviewed or exported without depending on one requirements platform.
Knowledge weighted by permanenceProposed, approved and released information do not carry the same authority.
Public workflow
From fragmented inputs to trusted delivery evidence.
A deliberately simplified view of the operating pattern: enough to show where assurance and human judgement sit, without publishing the implementation behind them.
EvidenceHuman decisionDeliveryTrusted knowledge
Analyse and reconcile
Conflicts, gaps, risks and test intent become visible.
Validate what moves forward
A named owner reviews the proposed direction.
Plan, build and test
Delivery proceeds against approved evidence.
Verify the result
Outcomes and coverage are checked before publication.
Publish trusted knowledge
Only approved, verified learning becomes durable.
Built for AI-capable teams
Your developers may already use AI. That is expected.
Keep Copilot, Claude, ChatGPT and the AI inside your delivery platform. Requirements Assurance does not compete with an individual developer’s assistant; it gives the team a shared, reviewable evidence chain around the work those tools help produce.
What existing assistants do well
- Draft stories, code and individual tests quickly
- Explain a file or summarise supplied context
- Suggest edge cases from the current prompt
- Accelerate implementation for an experienced developer
What the assurance layer adds
- Reconciles requirements and knowledge from multiple sources
- Shows which evidence is temporary, approved or released
- Links test cases, decisions, code and production behaviour
- Retains human approvals, revisions and reasons for change
Operating sequence
Structured analysis, with judgement kept visible.
STAGE 0Collect evidence
Read approved requirements, discussions, decisions, code and production knowledge.
STAGE 1Assess readiness
Surface conflicts, missing actors, dependencies, risks and unanswered questions.
STAGE 2Create tests
Prepare traceable functional, failure, boundary, permission and regression cases.
GATEHuman validation
Resolve material decisions and approve what is allowed to become authoritative.
STAGE 3Build and verify
Deliver against the approved change and linked test evidence.
STAGE 4Record and learn
Retain the release result, change reason and durable product knowledge.
Authority rule: no single tracking platform is assumed to hold the whole truth. Every evidence item retains its source, owner, status and effective scope; temporary context can inform analysis without silently overriding approved or released knowledge.
Knowledge with a known status
Relevant does not always mean authoritative.
The knowledge base keeps useful context available while making its permanence explicit. That prevents an old comment, a pending idea or an AI inference from quietly becoming a product rule.
Draft stories, meeting notes, comments, hypotheses and pending decisions. Useful to analysis, but time-bound, attributed and lower authority.
Accepted requirements, designs, decisions and test intent for a defined change or release. Authoritative within its approved scope.
Production behaviour, deployed configuration, verified tests, incidents and durable decisions. Highest weight when assessing the system that exists.
A practical first review
Start with one meaningful change, not a platform rollout.
Evidence inRequirements and decisionsMeetings, documents and messagesCode and production behaviour
Assurance layerReconcile → classify → test → approve
Evidence outTraceable test casesChange and approval recordWeighted delivery knowledge
What the review should demonstrate
- Requirements and relevant knowledge collected from the tools your team already uses
- Conflicts, missing decisions, actors, dependencies and risks made visible
- Functional, failure, boundary, permission and regression test cases created and traced
- Each source classified as temporary, approved or released knowledge
- Human decisions, revisions and reasons retained in an audit-ready change record
- An evidence pack that can be reviewed or exported independently of the source platform
Scoped from the real workflow
Price follows access and complexity—not an invented story count.
The effort changes with the number of source systems, repository access, requirement quality, test depth, release history and governance expectations. The first conversation selects one meaningful change and defines the evidence chain worth proving.
Evidence before claims
Measure whether the assurance layer improves delivery.
Material questions and conflicts resolved before commitment, with the approving owner recorded.
Requirements and risks with linked tests, including negative, permission, boundary and regression paths.
Changes that can be followed from request through decision, implementation, verification and released knowledge.
A low-risk first step
See one change traced from request to released evidence.
Bring a real upcoming change, however it is currently documented. We will identify what would need to be connected, tested and retained—without asking your team to abandon its existing AI or delivery tools.
Request an evidence-chain review →