Add company intelligence & decision engine with recommendations, scheduler jobs, frontend UI and CI

[Code-311]

Motivation

• Introduce company-level intelligence ingestion and surface actionable recommendations to drive next steps from the deterministic decision engine.
• Persist company signals and recommendations in the database to enable watchlist, alerting and prioritization workflows.
• Expose new APIs and surface these signals/recommendations in the UI and automated jobs so the product can react to external events.

Description

• Add database migrations 0003_company_signals and 0004_recommendations, new models CompanySignal and Recommendation, and wire them into backend/app/db/base.py.
• Implement company_intelligence service with RSS parsing and ingestion (backend/app/services/company_intelligence.py) and a decision engine that generates/upserts recommendations (backend/app/services/decision_engine.py).
• Expand API surface in backend/app/api/v1/routes.py with endpoints for GET /company-signals, POST /ingest/company-intelligence, GET/POST /recommendations and a detail endpoint, plus automatic recommendation refreshes after ingest/rescore/opportunity updates, and admin job hooks for company_intelligence and decision_engine.
• Add scheduled jobs company_intelligence_job and decision_engine_job in backend/app/jobs/scheduler.py and register them in start_scheduler().
• Update frontend to display company signals and recommendations across dashboard, admin, network, execution, opportunities and opportunity detail pages, and add admin buttons to run new jobs; add typed API helper return generics in frontend/src/lib/api.ts.
• Add unit tests for the new features: test_company_intelligence.py, test_decision_engine.py, and test_recommendations_api.py, and update backend packaging config (pyproject.toml) and project docs (HOWTO.md, README.md).
• Add a GitHub Actions CI workflow (.github/workflows/ci.yml) to run backend pytest -q and frontend npm test / npm run build on push and pull requests.

Testing

• Ran backend unit tests with pytest -q, which executed the new tests test_company_intelligence.py, test_decision_engine.py, and test_recommendations_api.py and they passed.
• Per CI configuration, frontend validation is set to run npm test and npm run build; the workflow file /.github/workflows/ci.yml was added to enable these checks on push and PRs.
• API surface was exercised by a FastAPI TestClient smoke test in test_recommendations_api.py, which returned HTTP 200 for the recommendations endpoints.

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