jeffsi-meet/deploy/meeting-intelligence

Meeting Intelligence System

A fully self-hosted, zero-cost meeting intelligence system for Jeffsi Meet that provides:

• Automatic meeting recording via Jibri
• Local transcription via whisper.cpp (CPU-only)
• Speaker diarization (who said what)
• AI-powered summaries via Ollama
• Searchable meeting archive with dashboard

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                      Netcup RS 8000 (Backend)                       │
│                                                                     │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────────────────┐ │
│  │   Jibri     │───▶│  Whisper    │───▶│      AI Processor       │ │
│  │  Recording  │    │ Transcriber │    │  (Ollama + Summarizer)  │ │
│  │  Container  │    │  Service    │    │                         │ │
│  └─────────────┘    └─────────────┘    └─────────────────────────┘ │
│         │                  │                        │               │
│         ▼                  ▼                        ▼               │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │                      PostgreSQL + pgvector                   │   │
│  └─────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘

Components

Service	Port	Description
PostgreSQL	5432	Database with pgvector for semantic search
Redis	6379	Job queue for async processing
Transcriber	8001	whisper.cpp + speaker diarization
API	8000	REST API for meetings, transcripts, search
Jibri	-	Recording service (joins meetings as hidden participant)

Deployment

Prerequisites

1. Docker and Docker Compose installed
2. Ollama running on the host (for AI summaries)
3. Jeffsi Meet configured with recording enabled

Setup

1. Copy environment file:

   cp .env.example .env
   

2. Edit .env with your configuration:

   vim .env
   

3. Create storage directories:

   sudo mkdir -p /opt/meetings/{recordings,audio}
   sudo chown -R 1000:1000 /opt/meetings
   

4. Start services:

   docker compose up -d
   

5. Check logs:

   docker compose logs -f
   

API Endpoints

Base URL: https://meet.jeffemmett.com/api/intelligence

Meetings

• GET /meetings - List all meetings
• GET /meetings/{id} - Get meeting details
• DELETE /meetings/{id} - Delete meeting

Transcripts

• GET /meetings/{id}/transcript - Get full transcript
• GET /meetings/{id}/transcript/text - Get as plain text
• GET /meetings/{id}/speakers - Get speaker statistics

Summaries

• GET /meetings/{id}/summary - Get AI summary
• POST /meetings/{id}/summary - Generate summary

Search

• POST /search - Search transcripts (text + semantic)
• GET /search/suggest - Get search suggestions

Export

• GET /meetings/{id}/export?format=markdown - Export as Markdown
• GET /meetings/{id}/export?format=json - Export as JSON
• GET /meetings/{id}/export?format=pdf - Export as PDF

Webhooks

• POST /webhooks/recording-complete - Jibri recording callback

Processing Pipeline

1. Recording - Jibri joins meeting and records
2. Webhook - Jibri calls /webhooks/recording-complete
3. Audio Extraction - FFmpeg extracts audio from video
4. Transcription - whisper.cpp transcribes audio
5. Diarization - resemblyzer identifies speakers
6. Embedding - Generate vector embeddings for search
7. Summary - Ollama generates AI summary
8. Ready - Meeting available in dashboard

Resource Usage

Service	CPU	RAM	Storage
Transcriber	8 cores	12GB	5GB (models)
API	1 core	2GB	-
PostgreSQL	2 cores	4GB	~50GB
Jibri	2 cores	4GB	-
Redis	0.5 cores	512MB	-

Troubleshooting

Transcription is slow

• Check CPU usage: docker stats meeting-intelligence-transcriber
• Increase WHISPER_THREADS in docker-compose.yml
• Consider using the tiny model for faster (less accurate) transcription

No summary generated

• Check Ollama is running: curl http://localhost:11434/api/tags
• Check logs: docker compose logs api
• Verify model is available: ollama list

Recording not starting

• Check Jibri logs: docker compose logs jibri
• Verify XMPP credentials in .env
• Check Prosody recorder virtual host configuration

Cost Analysis

Component	Monthly Cost
Jibri recording	$0 (local)
Whisper transcription	$0 (local CPU)
Ollama summarization	$0 (local)
PostgreSQL	$0 (local)
Total	$0/month