Predict. Simulate. Deliver.
AI-powered engineering execution intelligence for leaders who need to know whether a team and initiative can realistically deliver — before execution risk becomes a delivery failure.
| Live Demo | signalforge-o0m4.onrender.com/dashboard |
| API | signalforge-o0m4.onrender.com |
| Swagger | signalforge-o0m4.onrender.com/docs |
Engineering leaders often see delivery risk only after a project is already in motion.
The signals that matter are usually scattered — across repositories, work items, delivery systems, incidents, capability knowledge, project dependencies, and ownership structures. Status decks and spreadsheets can make an initiative look healthy while capability gaps, concentrated ownership, or a fragile dependency remain invisible.
SignalForge brings those signals together so teams can spot delivery risk earlier, explore interventions, and decide with clearer evidence before problems become expensive.
It evaluates delivery-system risk. It is not employee surveillance, performance ranking, hiring automation, or automated employment decision-making.
Assess capability coverage, project fit, and execution readiness — with readiness and confidence treated as separate signals.
Normalize engineering evidence into a tenant-scoped evidence model with provenance, so recommendations can be traced back to sources.
Connect teams, projects, repositories, dependencies, work items, incidents, and ownership relationships into a navigable delivery graph.
Explore decision-support simulations such as dependency slips, capability shortages, ownership concentration, and critical-resource availability changes. Scenarios are overlays for leadership reasoning — not causal predictions.
Generate evidence-grounded engineering leadership briefs with source binding, human review workflows, and deterministic fallback when live AI is unavailable.
Track system behavior, evidence quality, AI workflows, and review activity so operators can see how the intelligence layer is behaving.
Delivery readiness — capability coverage, project fit, risk, and team recommendation in one view.
Scenario intelligence — compare before/after impact when critical capacity changes.
AI Chief of Staff — evidence-grounded briefing for leadership questions.
Explainable reasoning — structured drivers behind a delivery outlook.
Engineering Systems
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Connector & Evidence Layer
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Normalized Enterprise Evidence
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Delivery Graph + Prediction + Scenario Intelligence
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AI Chief of Staff
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Human Review + Executive Decision Support
Signals enter through connectors and evidence ingestion, land in a normalized tenant-scoped model, and feed the delivery graph, readiness scoring, and scenario overlays. AI synthesizes grounded briefs for leaders; humans review and remain accountable for decisions.
SignalForge is built for environments where explainability and isolation matter as much as insight:
- Evidence-grounded AI with citation binding and deterministic fallback
- Delivery graph intelligence over teams, systems, and ownership
- Deterministic scenario simulation for decision support
- Delivery prediction infrastructure with honest estimate labeling (not promoted as a calibrated probability)
- Human review workflows that never silently rewrite scores
- Tenant isolation, JWT authentication, RBAC, and PostgreSQL Row-Level Security
- Auditability, observability, and AI-quality evaluation foundations
- Deterministic test paths that do not require live external LLM access
SignalForge originated in a Microsoft-focused engineering context and is designed to fit enterprise Microsoft environments.
In the product today: optional Azure OpenAI provider support with deterministic fallback, Entra OIDC JWT verification as a configured auth mode, and a GitHub REST polling connector for engineering evidence.
Designed for / not yet shipped as interactive production integrations: Microsoft Entra browser login, Azure Container Apps or App Service hosting, Azure Database for PostgreSQL as a production cutover, live Azure OpenAI production operation, Teams, Power BI, Copilot Studio, and Azure Marketplace publishing.
Microsoft has not endorsed this project.
Backend: FastAPI · Python · SQLAlchemy · PostgreSQL · Alembic · Pydantic
Frontend: Next.js · React · TypeScript · Tailwind · shadcn/ui
AI / Intelligence: Evidence-grounded briefs · Delivery graphs · Scenario simulation · Evaluation workflows · Optional Azure OpenAI
Engineering: Pytest · Vitest · Playwright · Ruff · GitHub Actions · Docker
Security: JWT · RBAC · PostgreSQL RLS · Tenant isolation · Gitleaks · Dependency auditing
Ingestion: GitHub-backed evidence polling (implemented). Jira and Azure DevOps HTTP connectors are not completed.
Engineering quality (verified baseline): Backend 997 · Frontend 43 · Playwright 8 · Remote PostgreSQL 24 · Production dependency audits at 0 known vulnerabilities (pip + npm).
NovaBank is a deterministic synthetic enterprise used to demonstrate SignalForge safely. It is not a customer.
The demo tenant is sized to feel like a real engineering organization:
- 48 engineers
- 14 initiatives
- 32 repositories
- 1,015 graph nodes / 1,362 graph edges after materialization
- 8 canonical delivery-risk scenarios
It is production-ineligible by design — a controlled dataset for demos, tests, and narrative walkthroughs.
SignalForge has a strong enterprise architecture and extensive automated validation. Several areas remain intentionally unclaimed:
- NovaBank data is synthetic
- The final enterprise build has not been validated in a real customer production environment
- Microsoft Entra interactive authentication is not yet implemented
- Jira HTTP integration is not yet implemented
- Azure DevOps HTTP integration is not yet fully implemented
- Delivery prediction is not promoted as a calibrated probability model
- Real customer outcome / ROI validation has not been established
- Production-scale performance limits have not been validated
SignalForge is being developed with a simple principle: intelligence should be explainable, evidence-backed, and useful to human decision-makers.