Case study · 2024
AIME: meeting intelligence and voice agents, end-to-end
An AI meeting and voice-agent platform (capture, transcribe, summarize, retrieve, and run live agents on top) built as a clean separation of a Python backend and a modern web operator console so each side can evolve independently.
Author · Maintainer
Stack
Python · FastAPI · LiveKit Agents · LangGraph · Postgres · Google APIs · Vite · React · TypeScript · Tailwind
Outcomes
- Full meeting-intelligence and voice-agent surface: capture, transcript, structured summary, retrieval over past meetings, and live agents on top.
- Clear API contract between backend and operator console so either side can be swapped or extended.
- Public companion repos for ideas explored in the voice and agentic work at scale.
What I owned
The full architecture: ingestion pipeline for audio, ASR with diarization, the structured summary layer, the storage and retrieval model for searching across past meetings, and the API the frontend talks to. The frontend is a deliberate companion: a clean way to show what the backend can do without leaking implementation details.
What shipped
A two-repo project (AIME-backend, AIME-frontend) you can stand up end-to-end. The backend is the system of record; the frontend is one consumer of it. Anything you can do in the UI is reachable from the API.
Lessons
Meeting intelligence is mostly a data shape problem, not an LLM problem. The interesting work is in how transcripts, speaker turns, summaries, and per-meeting facts get represented so that downstream features (search, briefings, follow-up generation) compose cleanly instead of fighting each other.