# MonGOD

- **Event:** [Agentic Memory & Context Engineering Hackathon](https://cerebralvalley.ai/e/mongoDB-hackathon)
- **When:** Sat, Oct 11 at 9:00 AM – 10:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Placement:** Finalist
- **Team:** [Janavi Srinivasan](https://cerebralvalley.ai/u/JANS), [Arnav Dewan](https://cerebralvalley.ai/u/arnavdewan), [Thilak Shekhar Shriyan](https://cerebralvalley.ai/u/thilakshriyan), [Hrishikesh Athreya](https://cerebralvalley.ai/u/hrishi)
- **GitHub:** https://github.com/janavi2001/Seeded_Rag_Dashboard https://github.com/Hrishikesh-Athreya/mongo-db-hackathon
- **Demo video:** https://youtu.be/L6t4HBlA9DA
- **Gallery:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery/70

Incident Copilot is a seeded-RAG assistant for on-call engineers. It uses MongoDB Atlas Vector Search, Voyage embeddings with reranking, and Google Gemini to answer operational questions fast and accurately. Instead of running full retrieval each turn, it builds a reusable “seed” per incident: a summary plus top reranked snippets from runbooks, postmortems, and dashboards. Follow-ups reuse the seed unless topic drift is detected via cosine similarity; then it rebuilds automatically. A React dashboard shows answers, citations, and context with metrics for latency, tokens, seed hits, and drifts. Example: “Kafka consumer lag is spiking—what now?” The first turn retrieves and reranks; later turns answer from cache, cutting latency and token spend while improving focus and factuality. Result: calmer pagers, faster fixes, and reliable, source-linked help.

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