Add company intelligence + decision engine (models, services, API, UI), migrations and CI

[Code-311]

Motivation

• Introduce company-level intelligence ingestion and a decision engine to produce actionable recommendations derived from opportunity and company signals.
• Persist company signals and recommendations to support observability, automated scheduling, and UI surfaces.
• Surface recommendations and company signals in the dashboard and admin UI so users can act on them.
• Add CI to validate backend unit tests and frontend build/test on push/pull requests.

Description

• Added Alembic migrations 0003_company_signals and 0004_recommendations to create company_signals and recommendations tables with indexes.
• Implemented SQLAlchemy models and Pydantic schemas: CompanySignal and Recommendation plus registration in app/db/base.py.
• Implemented company_intelligence service that parses RSS-like payloads, normalizes events, persists CompanySignal rows, and creates related opportunity Signal entries.
• Implemented decision_engine service that scores and upserts Recommendation rows (multiple recommendation types) and expires stale items.
• Wired services into the API: new endpoints and handlers in backend/app/api/v1/routes.py for listing/ingesting company signals and recommendations and for triggering a recommendations refresh, plus calls to refresh recommendations from ingest/rescore flows.
• Added scheduler jobs in backend/app/jobs/scheduler.py for company_intelligence and decision_engine and exposed them via the admin job runner.
• Frontend updates: dashboard, admin, network, execution, opportunity detail pages now request and render company signals and open recommendations, and admin can run the new jobs and refresh recommendations.
• Documentation and ops: added HOWTO.md, updated README.md, and added GitHub Actions CI workflow .github/workflows/ci.yml to run backend pytest -q, frontend npm test, and frontend npm run build on push/pull_request.

Testing

• Added backend unit tests: test_company_intelligence.py, test_decision_engine.py, and test_recommendations_api.py under backend/app/tests and executed them with pytest -q; they passed.
• CI configuration runs pytest -q for the backend and npm test and npm run build for the frontend as part of the GitHub Actions pipeline, and those steps were validated locally during the rollout and succeeded.

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