# Project Knowledge

## Vision

The project creates a complete pipeline that transforms spoken meetings into structured organizational knowledge.

The transcript is the canonical source.

AI-generated summaries are reproducible artefacts.

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## Design Principles

Offline-first where practical.

Replaceable AI components.

Vendor independence.

Modular architecture.

Small focused modules.

Simple interfaces.

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## Core Pipeline

Recorder

↓

Transcription

↓

Speaker Identification

↓

LLM Analysis

↓

Knowledge Extraction

↓

Storage

↓

Export

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## AI Components

Recorder

Responsible only for recording.

No AI.

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Transcription

Responsible only for speech-to-text.

No summarization.

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Speaker Identification

Responsible only for identifying speakers.

Must not modify transcript text.

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LLM Analysis

Responsible for:

- Summary

- Decisions

- Action Items

- Risks

- Questions

- Knowledge extraction

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Storage

Stores:

Audio

Transcript

Metadata

AI Results

Knowledge Objects

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## Architectural Rule

Every module has exactly one responsibility.

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## Transcript Policy

Never modify the original transcript.

Corrections must create a new derived version.

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## AI Output Policy

Every AI output should be reproducible.

Prompt version should be stored.

Model should be stored.

Timestamp should be stored.

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## Long-Term Goal

Every meeting becomes searchable organizational knowledge.

No information should be lost after the meeting.

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## Engineering Principles

The project follows an architecture-first development approach.

Before implementing a feature:

- define the domain model
- define module boundaries
- document architectural decisions

Implementation is intentionally delayed until the architecture is considered sufficiently stable.