1.8 KiB
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.
Design Principles
Offline-first where practical.
Replaceable AI components.
Vendor independence.
Modular architecture.
Small focused modules.
Simple interfaces.
Core Pipeline
Recorder
↓
Transcription
↓
Speaker Identification
↓
LLM Analysis
↓
Knowledge Extraction
↓
Storage
↓
Export
AI Components
Recorder
Responsible only for recording.
No AI.
Transcription
Responsible only for speech-to-text.
No summarization.
Speaker Identification
Responsible only for identifying speakers.
Must not modify transcript text.
LLM Analysis
Responsible for:
-
Summary
-
Decisions
-
Action Items
-
Risks
-
Questions
-
Knowledge extraction
Storage
Stores:
Audio
Transcript
Metadata
AI Results
Knowledge Objects
Architectural Rule
Every module has exactly one responsibility.
Transcript Policy
Never modify the original transcript.
Corrections must create a new derived version.
AI Output Policy
Every AI output should be reproducible.
Prompt version should be stored.
Model should be stored.
Timestamp should be stored.
Long-Term Goal
Every meeting becomes searchable organizational knowledge.
No information should be lost after the meeting.
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.