Complete rewrite from health domain to post-harvest market intelligence
for Tamil Nadu smallholder farmers. Same 6-step pipeline architecture
(ingest → extract → reconcile → forecast → optimize → recommend),
new domain: conflicting mandi price data → reconciled prices →
sell-now-or-wait recommendations with credit readiness assessment.
Backend: 15 real Tamil Nadu mandis, 10 commodities, real Agmarknet API
(data.gov.in), eNAM scraper, NASA POWER weather, XGBoost price forecaster,
Claude agents with rule-based fallbacks, FAISS+BM25 RAG.
Frontend: 5 pages (Markets, Forecast, Sell Advisor, Pipeline, Data),
amber/saffron theme, Tamil Nadu map, Joyride tour, credit readiness UI.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Expand XGBoost demand model from 13 to 20 features (RMSE 5.8→3.3, R²=0.998)
- Add residual correction model (MOS pattern: model-correcting-model)
- Add isolation forest anomaly detection on consumption patterns
- Upgrade RAG from TF-IDF to sentence-transformers (all-MiniLM-L6-v2)
- Expand knowledge base from 33 to 101 chunks across 15 categories
- Add Neon PostgreSQL persistence layer (5 tables)
- Add evaluation suite (44 pytest tests)
- Wire new components into pipeline and API
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Full-stack AI pipeline for health commodity forecasting and procurement optimization.
Backend: FastAPI + Claude agents for extraction, healing, reconciliation, demand forecasting, and procurement.
Frontend: React + Vite + Tailwind dashboard with guided tour.
CI: GitHub Actions for backend/frontend validation + weekly pipeline trigger.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
