08. Build an AI Meeting Assistant

Introduction

Build an AI meeting assistant that joins meetings, transcribes speech in real-time, generates meeting summaries, extracts action items, and integrates with calendars and messaging platforms.

Meeting overload is a top productivity drain. An AI meeting assistant captures every discussion, generates concise summaries, and tracks action items — so teams never miss a detail.

Problem Statement

Teams spend 15+ hours/week in meetings. Notes are incomplete, action items get lost, and absent team members miss context. An AI meeting assistant should:

Join and transcribe meetings in real-time
Generate structured meeting summaries
Extract action items with owners
Integrate with calendars (Google, Outlook) and messaging (Slack)

Business Use Case

A remote-first company with 200 employees needs an AI meeting assistant that transcribes all meetings, generates summaries for absent members, and tracks action items across teams.

Requirements

Functional Requirements

#	Feature	Description
FR1	Meeting transcription	Real-time speech-to-text
FR2	Speaker diarization	Identify who said what
FR3	Meeting summary	Auto-generated meeting notes
FR4	Action item extraction	Tasks with owners and deadlines
FR5	Calendar integration	Auto-join meetings from calendar
FR6	Slack integration	Post summaries to channels
FR7	Search past meetings	Full-text search over transcripts
FR8	Video recording	Optional recording with transcription

Non-Functional Requirements

#	Requirement	Target
NFR1	Transcription latency	< 2s real-time delay
NFR2	Accuracy	> 95% WER (word error rate)
NFR3	Speaker accuracy	> 90% correct speaker identification
NFR4	Processing time	Summary generated within 1 min of meeting end
NFR5	Scalability	Support 100 concurrent meetings

Technology Stack

Layer	Technology	Purpose
Frontend	Next.js + Tailwind	Dashboard, transcript viewer
Backend	FastAPI (Python)	API server, WebSocket management
Speech-to-text	Whisper (OpenAI) / Deepgram	Real-time transcription
AI	GPT-4o / Claude	Summarization, action extraction
Database	PostgreSQL	Meeting records, transcripts
Vector DB	pgvector	Transcript search
Queue	Celery + Redis	Async processing
Calendar	Google Calendar API, Outlook API	Meeting scheduling
Messaging	Slack API, Teams API	Summary distribution

Architecture

flowchart TD
    subgraph INPUT["Input Sources"]
        MIC["Microphone\nBot joins meeting"]
        CAL["Calendar\nAuto-detect meetings"]
        REC["Recording\nUploaded audio/video"]
    end
    subgraph PROCESS["Processing"]
        STT["Speech-to-Text\nWhisper/Deepgram"]
        DIARIZE["Speaker Diarization\nIdentify speakers"]
        TRANSCRIBE["Live Transcript\nReal-time stream"]
    end
    subgraph AI["AI Services"]
        SUMM["Summarization\nMeeting summary"]
        ACTION["Action Items\nTask extraction"]
        HIGHLIGHTS["Key Moments\nImportant clips"]
    end
    subgraph OUTPUT["Output"]
        NOTES["Meeting Notes\nStructured summary"]
        TASKS["Action Items\nWith owners"]
        SEARCH["Searchable Archive"]
        INTEGRATE["Slack/Email\nDistribution"]
    end

    MIC --> STT
    CAL --> STT
    REC --> STT
    STT --> DIARIZE
    DIARIZE --> TRANSCRIBE
    TRANSCRIBE --> SUMM
    TRANSCRIBE --> ACTION
    TRANSCRIBE --> HIGHLIGHTS
    SUMM --> NOTES
    ACTION --> TASKS
    HIGHLIGHTS --> NOTES
    TRANSCRIBE --> SEARCH
    NOTES --> INTEGRATE

    style INPUT fill:#3b82f6,color:#fff
    style PROCESS fill:#f59e0b,color:#fff
    style AI fill:#8b5cf6,color:#fff
    style OUTPUT fill:#22c55e,color:#fff

Meeting Lifecycle

sequenceDiagram
    participant Bot as Meeting Bot
    participant STT as Speech-to-Text
    participant AI as AI Service
    participant Store as Database
    participant Slack as Slack

    Note over Bot: Before Meeting
    Bot->>Bot: Read calendar, join meeting link

    Note over Bot,STT: During Meeting
    Bot->>STT: Stream audio
    STT->>STT: Real-time transcription
    STT-->>Bot: Text with speaker labels
    Bot->>Store: Store live transcript

    Note over AI: After Meeting
    Bot->>AI: Process full transcript
    AI->>AI: Generate summary
    AI->>AI: Extract action items
    AI->>AI: Identify key decisions

    AI-->>Bot: Summary + Actions + Decisions

    Bot->>Store: Save meeting record
    Bot->>Slack: Post summary to channel

    Note over Bot: Done in < 60s after meeting

API Design

Method	Endpoint	Purpose
POST	`/api/meetings/join`	Bot joins a meeting
POST	`/api/meetings/schedule`	Schedule bot for future meeting
GET	`/api/meetings`	List past meetings
GET	`/api/meetings/{id}`	Get meeting details
GET	`/api/meetings/{id}/transcript`	Get full transcript
GET	`/api/meetings/{id}/summary`	Get AI summary
GET	`/api/meetings/{id}/actions`	Get action items
GET	`/api/search?q=`	Search across meetings
POST	`/api/integrations/slack`	Configure Slack integration
POST	`/api/integrations/calendar`	Configure calendar

Deployment

flowchart TD
    subgraph BOT["Bot Service"]
        JOINER["Meeting Joiner\nPuppeteer/API"]
        AUDIO["Audio Stream\nWebSocket"]
    end
    subgraph PROCESSING["Processing"]
        STT_WORKERS["STT Workers\nGPU-enabled"]
        SUMMARY_WORKERS["Summary Workers"]
    end
    subgraph STORE["Storage"]
        DB["PostgreSQL"]
        S3["Audio Recordings"]
    end

    BOT --> PROCESSING
    PROCESSING --> STORE

    style BOT fill:#3b82f6,color:#fff
    style PROCESSING fill:#8b5cf6,color:#fff
    style STORE fill:#f59e0b,color:#fff

Evaluation

Metric	Method	Target
Transcription accuracy	WER	< 5%
Speaker identification	% correct labels	> 90%
Summary quality	Human rating	> 4.0/5
Action item recall	% of actual action items captured	> 80%
Processing time	Time from meeting end to summary	< 60s

Security

Concern	Implementation
Meeting privacy	Bot only joins meetings it’s invited to
Data retention	Transcripts auto-deleted after 30/60/90 days
Compliance	GDPR — right to delete meeting data
Access control	Per-meeting access permissions
Encryption	TLS for all data, encryption at rest

Interview Questions

Q: Design the real-time transcription pipeline for 1000+ concurrent meetings.

Pipeline: (1) Audio ingestion — WebSocket per meeting sending audio chunks to a Kafka topic, (2) STT workers — GPU pool running Whisper/Deepgram, each handling multiple streams, (3) Speaker diarization — Separate model clusters speakers, (4) Live transcript — Stream to frontend via WebSocket, (5) Recording — Save audio to S3 for post-processing.

Summary

Feature	Implementation
Transcription	Whisper/Deepgram real-time STT
Speaker ID	Diarization model
Summary	GPT-4o post-meeting processing
Action items	LLM extraction with owner detection
Calendar	Google/Outlook API
Distribution	Slack/Teams/Email

Previous: 07 — Build an AI Document Assistant

Next: 09 — Build an AI Email Assistant

Related Projects: