Release Intelligence

Explainable confidence for critical release decisions.

Live application: release-intelligence.uthamkumar.info

Release Intelligence is a public enterprise reference implementation for bringing requirements, delivery, quality, security, dependencies, and operational evidence into one decision-ready release view. It is positioned as an engineering operating model and decision system—not as a coding exercise.

Portfolio provenance: This public reference implementation demonstrates patterns, frameworks, and operating-model concepts developed through enterprise technology-program work during 2023–2026. The implementation, names, dates, organizations, and data in this repository are fictional and synthetic.

The operating problem

Release decisions often happen across fragmented trackers, presentations, chat threads, and judgment calls. Leaders can see status, but not always the evidence behind it, the confidence of the recommendation, or the fastest action to improve the outcome.

Release Intelligence turns that fragmented evidence into three things:

1. a shared, explainable release-confidence signal;
2. a focused set of risks, blockers, and dependencies with accountable owners; and
3. a concise decision brief that keeps humans responsible for the final call.

Current capabilities

• Portfolio Control Tower — compare confidence, decision posture, priority signals, systemic risk themes, and upcoming forums across releases.
• Executive Release Overview — decision posture, target date, portfolio, ownership, and leading signals.
• Release Readiness Scorecard — six weighted readiness dimensions with evidence and trend context.
• Risk & Blocker Intelligence — severity-ranked exposure, owner, due date, impact, and status.
• Dependency View — critical inbound and outbound commitments around the release.
• Release Timeline & Trend — milestone path and six-review confidence trajectory.
• AI Release Brief — an evidence-grounded executive narrative with a deterministic, no-API-key demo mode.
• Explainable confidence — weighted inputs, evidence penalty, safety caps, top score drivers, and recommended actions.

Release Confidence at a glance

The score is a decision aid, not an opaque prediction:

weighted readiness − evidence-quality penalty → safety-gate cap → final confidence

Dimension	Weight
Requirements	15%
Development	20%
Testing	25%
Security	15%
Dependencies	10%
Operational readiness	15%

Mandatory safety conditions cap confidence even when the weighted average looks healthy. For example, a critical blocker caps confidence at 59 and an open critical security finding caps it at 49. See the full methodology.

Application architecture

flowchart LR
    A["Synthetic release fixtures"] --> B["Typed confidence engine"]
    B --> C["Next.js / React decision workspace"]
    C --> D["Executive overview"]
    C --> E["Readiness, risk & dependencies"]
    C --> F["Timeline & deterministic AI brief"]
    G["Future evidence adapters"] -. "normalized signals" .-> A

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V1 deliberately uses static JSON and a client-side calculation layer. A backend would add operational weight without improving the public demonstration. The seams are documented so future releases can add authenticated evidence adapters or persistence without rewriting the decision model.

The repository is dual-target: native Next.js for Vercel and a Cloudflare-compatible vinext build for OpenAI Sites.

Run locally

Prerequisites: Node.js 22+ and pnpm 11+.

pnpm install
pnpm dev

pnpm dev runs the native Next.js application used by Vercel. Use pnpm dev:sites only when validating the Sites adapter.

Validate the full project with:

pnpm check

Repository map

release-intelligence/
├── app/                  # Interactive decision workspace
├── data/                 # Synthetic release scenarios
├── lib/                  # Explainable confidence engine
├── docs/                 # Product, operating model, architecture, and roadmap
├── public/               # Public presentation assets
├── tests/                # Rendered-output checks
└── .github/              # CI and contribution workflow

Product documentation

• Product charter, users, use cases, and information architecture
• Release Confidence methodology
• Synthetic data model
• Application architecture
• V1 plan, milestones, versioning, and README strategy
• Vercel deployment guide

Deploy with Vercel

Push the main branch to a GitHub repository named release-intelligence, then import that repository from the Vercel dashboard. Vercel detects the native Next.js build and requires no environment variables for the synthetic demo. See the deployment guide for the one-time settings.

Version roadmap

Version	Milestone	Outcome
v0.1.0	Decision workspace	Complete V1 demo with six views and synthetic scenarios
v0.2.0	Portfolio comparison	Current: compare releases, systemic patterns, and decision calendar
v0.3.0	Scenario intelligence	Simulate mitigations and forecast confidence movement
v0.4.0	Evidence adapters	Pluggable, read-only integration contracts with synthetic fixtures
v1.0.0	Reference standard	Stable scoring contract, governance guide, accessibility, and deployment hardening

Responsible AI and data statement

The AI brief in V1 is a deterministic template grounded in visible structured evidence and works without an API key. Future model-assisted modes must preserve citations to source signals, label generated content, support a no-key fallback, and leave final release authority with named human decision makers.

No real company, customer, product, control, credential, or confidential operational data is included.

License

MIT. See LICENSE.