# Architecture

## Purpose

The Meeting Knowledge Assistant transforms recorded meetings into structured organizational knowledge through a modular processing pipeline.

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# Design Principles

- Architecture first
- Offline first where practical
- Immutable source data
- Replaceable AI components
- Small, focused modules
- Explicit interfaces
- Reproducible AI outputs

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# High-Level Pipeline

Recording
    ↓
Transcription
    ↓
Speaker Diarization
    ↓
Working Transcript
    ↓
LLM Analysis
    ↓
Knowledge Extraction
    ↓
Export

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# Domain Model

Meeting
│
├── Recording
├── Transcript
│     ├── Canonical
│     └── Working
├── Speakers
├── Participants
├── AI Artifacts
├── Knowledge Objects
├── Attachments
└── Exports

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# Module Responsibilities

## Recorder

Responsible only for capturing audio.

Input:
None

Output:
Recording

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## Transcription

Responsible only for speech-to-text conversion.

Input:
Recording

Output:
Canonical Transcript

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## Diarization

Responsible only for speaker identification.

Input:
Recording + Canonical Transcript

Output:
Working Transcript

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## LLM

Responsible for semantic analysis.

Input:
Transcript

Output:
AI Artifacts

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## Knowledge Extraction

Responsible for creating structured knowledge.

Input:
AI Artifacts

Output:
Knowledge Objects

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## Export

Responsible for creating user-facing documents.

Input:
Knowledge Objects

Output:
Markdown
PDF
DOCX

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# Artifact Lifecycle

Recording

↓

Canonical Transcript

↓

Working Transcript

↓

AI Artifacts

↓

Knowledge Objects

↓

Exports

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# Storage Strategy

The domain model is independent of the storage backend.

Possible implementations:

- File System
- SQLite
- PostgreSQL
- Cloud Storage

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# Future Extensions

- Live transcription
- Video processing
- OCR
- Semantic search
- Knowledge graph
- Company glossary
- Multi-language meetings
- Local LLM support