Minute – Agentic S/CRAG AI Meeting Co-Host for BFSI
VNPT AI Hackathon 2025 | Track 1 – Desktop/Web + GoMeet/Google Meet Add-in
Minute standardizes the meeting lifecycle for BFSI/LPBank enterprises: pre-meeting context gathering, real-time in-meeting assistance, and post-meeting minutes + action items generation—all with citations, audit trails, and access control. Built with SAAR (Self-aware Adaptive Agentic RAG) architecture featuring stage-aware routing (Pre/In/Post), real-time WS pipeline (audio → SmartVoice STT → session bus → live transcript/recap/ADR), permission-aware RAG with pgvector, and tool-calling with human-in-the-loop confirmation.
Timeline
2025
Type
Competition
Status
completed
Outcome / Impact
- •Built end-to-end AI meeting workflow: Pre-meeting (agenda + pre-read) → In-meeting (live transcript, recap, ADR extraction) → Post-meeting (executive summary, MoM, task sync)
- •Implemented SAAR architecture with stage-aware LangGraph routing, graded RAG retrieval, and self-corrective loops
- •Designed real-time WS pipeline with SmartVoice STT integration and tool-calling (create task, schedule, attach docs) with audit logging
- •Data modeling with pgvector embeddings, document ingestion (OCR/SmartReader), and optimized RAG queries
Tech / Skills
Project Media
Demo video and visual walkthrough for this project.
Demo Video
Open source videoCertificates (1)
VNPT AI Hackathon 2025 - 3rd Prize Certificate
Case Study
1) Context / Problem
Enterprise meetings in BFSI consume significant time but often fail to produce clear decisions, accountable action items, and traceable outcomes. Manual minute-taking is slow, error-prone, and lacks audit trails. LPBank needed a solution that integrates with existing workflows while meeting strict compliance requirements.
2) Your Role
As Developer, Data Engineer, and AI Engineer, I was a core contributor to the full-stack implementation. Designed the data model and pgvector schema, built the document ingestion pipeline (OCR/SmartReader), optimized RAG queries with metadata filtering, contributed to the LangGraph orchestration, and collaborated closely on backend API and real-time WebSocket features.
3) Approach
Built SAAR (Self-aware Adaptive Agentic RAG) architecture with: (1) Stage-aware LangGraph router for Pre/In/Post meeting phases, (2) Real-time WebSocket pipeline for audio → STT → session bus → live transcript, (3) Permission-aware RAG with pgvector + ACL filters, (4) Tool-calling with human-in-the-loop for task creation and scheduling.
4) Result / Impact
Delivered a product-ready Minute demo with end-to-end Pre/In/Post workflow, real-time transcription with ADR extraction, grounded RAG Q&A with citations, and audit-ready structured outputs. The solution was recognized in VNPT AI Hackathon 2025 Track 1.
5) Learnings
Learned to balance real-time latency requirements with LLM quality, implement effective graded retrieval strategies, and design for enterprise compliance. Would explore streaming LLM responses and more aggressive caching for production.
6) Links
See links above.