Add company intelligence and decision engine (models, APIs, scheduler, frontend, CI, migrations)

[Code-311]

Motivation

• Introduce company-level intelligence ingestion and lightweight RSS parsing to surface company signals alongside opportunity signals.
• Provide a deterministic decision engine to generate ranked, actionable recommendations (priorities, follow-ups, network actions, watchlist escalations) from opportunities, signals, and network context.
• Surface signals and recommendations in the UI and run them on a schedule with observability and CI validation.

Description

• Added new DB models and schemas: CompanySignal and Recommendation with corresponding Alembic migrations (0003_company_signals, 0004_recommendations).
• Implemented app.services.company_intelligence to parse/normalize RSS-style feeds and persist company_signals, and app.services.decision_engine to synthesize and upsert recommendations; both integrate with existing signal/opportunity models.
• Extended API in app.api.v1.routes with endpoints for GET /company-signals, POST /ingest/company-intelligence, GET/POST /recommendations and wired automatic recommendation refreshes after ingestion/rescore/opportunity changes and an admin job trigger for company_intelligence and decision_engine jobs.
• Added scheduler jobs in app.jobs.scheduler for company_intelligence and decision_engine and recorded job runs via JobRun observability hooks.
• Frontend updates to dashboard, admin, network, execution, opportunity detail, and admin pages to display company signals and recommendations and allow manual refreshes; added UI controls for running new jobs.
• Added tests under backend/app/tests covering company intelligence parsing and ingestion, decision engine generation and expiration behavior, and basic recommendations API existence; updated backend/pyproject.toml to include package discovery and exported models in app.db.base.
• Added HOWTO.md with operational/runbook details and a GitHub Actions ci.yml to validate backend (pytest) and frontend (npm test + build) on push and PR.

Testing

• Ran backend unit tests with pytest -q, including test_company_intelligence.py, test_decision_engine.py, and test_recommendations_api.py, and they passed.
• Added CI workflow /.github/workflows/ci.yml to run pytest for the backend and npm test plus npm run build for the frontend on push and pull requests.
• New tests exercise RSS parsing, ingest-to-signal linking, recommendation generation, expiration of stale open recommendations, and API endpoint availability and returned status codes.

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