When this diagram scrolls into view, a short tour runs by itself.
You can still tap any source or node anytime to explore on your own.
Hotjar → Knowledge Graph
Session recordings reveal real user flows. AI extracts the paths users actually take — including rage clicks and drop-offs.
Checkout flow — 3 rage clicks detected
→ 8 verification checks linked to real user paths
→ 2 bugs linked (payment timeout)
Mixpanel → Knowledge Graph
Funnel analytics show where users drop off. AI generates tests for every step in the funnel — especially the leaky ones.
Payment funnel — 40% drop-off at step 3
→ 12 risk-mapped verifications across the funnel
→ 3 API endpoints at risk
Jira → Knowledge Graph
Requirements and user stories become graph nodes. Every story links to tests, code, and designs automatically.
PROJ-142: Add coupon field
→ 5 verifications linked · 2 need update
→ CartService.js, PricingAPI
Figma → Knowledge Graph
Design screens map to requirements and visual regression tests. When designs change, affected tests are flagged.
Checkout redesign v3 — 14 screens
→ 14 visual verifications against live screens
→ 3 screens differ from Hotjar flows
Swagger → Knowledge Graph
API specs auto-generate endpoint tests. Schema changes trigger impact analysis across the entire graph.
POST /api/checkout — schema changed
→ 6 verifications affected by schema change
→ 2 code files need review
Bug reports → Knowledge Graph
Every bug links to the code, tests, and requirements it touches. See the full blast radius of any defect instantly.
BUG-89: Payment timeout on mobile
→ Linked to PROJ-142, /api/checkout
→ 3 verifications now guard this path
PR Validation → Knowledge Graph
Every PR validation run feeds risk scores, blast radius, component health, and affected tests back into the graph — building a living map of release risk across the entire codebase.
PR #278 — order schema migration · BLOCK · 82/100
→ 3 high-risk components flagged
→ Release Confidence updated: 66/100 · HOLD
Engineering Memory → Knowledge Graph
Past failures, clustered patterns, and proven fixes are embedded and semantically matched against new PRs. When the same component fails again, the graph surfaces the prior fix instantly.
6 failure patterns clustered · 7 resolutions stored
→ HIGH match on PR #278 — seen before in OrderService
→ Top fix: "wrap migration in explicit transaction"
Requirements node
Click any requirement to see which tests cover it, which code implements it, and which bugs affect it.
24 requirements imported
→ 89% verification coverage
→ 3 uncovered (high risk)
Verifications node
Every test traces back to a requirement, code file, and user flow. No orphan tests, no blind spots.
142 active verifications
→ 98% pass rate (last run)
→ 12 generated from real user flows
Code files node
See which tests cover each file. When code changes, impact analysis shows exactly what's at risk.
38 files tracked
→ 4 files with no verification coverage
→ 2 files linked to open bugs
API endpoints node
Auto-imported from Swagger. Schema changes trigger alerts across connected tests and requirements.
18 endpoints monitored
→ 52 API verifications
→ 1 breaking change detected
Figma screens node
Design screens link to visual regression tests. When Figma updates, baselines refresh automatically.
14 screens synced
→ 14 visual verifications active
→ 2 screens updated this week
PR Risk node
Each PR validation populates this node with a risk score, blast radius, component health snapshot, and impacted test list. The graph uses this to route re-verification and update the Release Confidence score.
4 PRs validated · avg risk 40/100
→ 1 BLOCK · 17 components tracked
→ Engineering Memory HIGH on 1 PR
Release Signal node
The final output of the Release Intelligence Layer. Aggregates all PR risk scores, component health trends, and Engineering Memory signals into a single Release Confidence score and a clear verdict.
Release Confidence: 66/100
→ Verdict: HOLD — ADDRESS WARNINGS
→ 1 BLOCK PR must be resolved before deploy
