# Roadmap

No dates are assigned. Phases describe dependency order, not release promises.

## Phase 1 - Stable Local Extraction

Goal:

- Establish reliable per-chunk extraction behavior for core meeting semantics.

Deliverables:

- Stronger Gold Standard coverage across facts, positions, decisions, todos,
  questions and technical details.
- Gold coverage for responsibility attribution: discussion, objection, role
  proximity and department mention must not become ownership.
- Improved category prompts.
- Repeatable evaluation workflow.
- Documented prompt experiment log.

Prerequisites:

- Existing chunk extraction flow.
- Existing Gold Standard runner and methodology.

Out of scope:

- Full-transcript LLM extraction.
- Larger context-window strategy changes without an explicit experiment.
- Canonicalization, semantic consolidation or final protocol rendering.

## Phase 2 - Deterministic Canonicalization

Goal:

- Convert independent chunk extraction JSON into a validated, normalized,
  evidence-bearing intermediate representation without semantic guessing.

Deliverables:

- Canonicalizer V1 implemented in Python.
- Stable source references and IDs.
- Normalized category names and basic field structure.
- Safe deterministic cleanup.
- Exact duplicate grouping where unambiguous.
- Preservation of all source evidence.

Prerequisites:

- Stable local extraction baseline.
- Agreement on the extraction object shape that should be canonicalized.

Current status:

- Implemented as `meeting_lab.consolidation.canonicalize`.

Out of scope:

- Uncertain semantic merging.
- Topic synthesis.
- Protocol writing.
- LLM calls.

## Phase 3 - Semantic Consolidation

Goal:

- Merge canonicalized extraction objects into a coherent semantic meeting
  representation while preserving evidence and uncertainty.

Deliverables:

- Semantic Consolidator V0 using the local LLM for facts-only duplicate
  detection.
- Semantically equivalent fact statement merging.
- Evidence preserved from all contributing chunks.
- Complete source fact coverage validation.
- Later broader semantic consolidation with topic grouping, contradiction and
  uncertainty markers, durable/transient separation and Canonical Meeting
  Knowledge preparation.

Prerequisites:

- Canonicalizer V1 output with stable IDs and source references.
- Gold or benchmark cases that expose duplication and category shifts.

Current status:

- Semantic Consolidator V0 is implemented and experimentally validated for
  facts-only conservative merging.
- The first accepted benchmark merged one correct pair among 33 facts and left
  31 singleton groups.

Out of scope:

- Direct protocol writing.
- Topic synthesis in V0.
- Processing decisions, action items, questions, positions or technical details
  in V0.
- Canonical Meeting Knowledge generation in V0.
- Deriving output views from one another.
- Retrieval or RAG integration.

## Phase 4 - Canonical Meeting Knowledge

Goal:

- Define and implement the semantic intermediate model that becomes the source
  of truth for downstream outputs.

Deliverables:

- Canonical Meeting Knowledge schema.
- Source evidence and traceability fields.
- Clear distinction between durable knowledge and meeting-specific actions.
- Migration path from consolidated extraction JSON into the canonical model.

Prerequisites:

- Semantic consolidation behavior that preserves evidence and uncertainty.
- Agreement on required semantic categories.

Out of scope:

- GUI.
- Export formats beyond those needed to validate the model.
- Knowledge-system storage design.

## Phase 5 - Output Views

Goal:

- Render purpose-specific outputs from Canonical Meeting Knowledge without
  changing meaning.

Deliverables:

- Working Protocol / Arbeitsprotokoll renderer.
- Distribution Protocol / Verteilerprotokoll renderer.
- Knowledge Objects / Wissensdatenbankeintrag renderer or structured export.
- Later additional views such as action lists.
- Tests or checks showing that output views are parallel renderings of the same
  canonical model.
- Default output-language policy: rendered protocols normally match the
  dominant source language unless explicitly requested otherwise.

Prerequisites:

- Implemented Canonical Meeting Knowledge.
- Clear audience and completeness rules for each output view.

Out of scope:

- Additional analysis during rendering.
- Deriving one output view from another.
- Retrieval integration.

## Phase 6 - Review and Quality Control

Goal:

- Add optional review stages that improve omission detection, consistency and
  model selection.

Deliverables:

- Optional whole-transcript review.
- Omission detection.
- Consistency checks.
- Model comparison workflow.
- Hardware and runtime benchmarks.

Prerequisites:

- Stable extraction, semantic consolidation and canonical model.
- Representative test meetings.

Out of scope:

- Automatic acceptance of review suggestions without evidence.
- Product UI work.
- Cloud deployment.

## Phase 7 - Productization

Goal:

- Turn the validated pipeline into a usable local workflow.

Deliverables:

- Recording/transcription workflow.
- FFmpeg integration.
- Meeting metadata capture.
- Participant entry.
- GUI.
- Stable deployment process.
- Export workflows.

Prerequisites:

- Stable pipeline stages and output views.
- Clear operational requirements for local use.

Out of scope:

- Enterprise knowledge retrieval.
- Future Meeting Assistant integration beyond export contracts.
- Cloud-first architecture.

## Phase 8 - Knowledge-System Integration

Goal:

- Reuse durable meeting knowledge in broader knowledge systems.

Deliverables:

- Structured Knowledge Objects.
- Retrieval-ready storage format.
- Future RAG integration path.
- Reuse contracts for Meeting Assistant and other knowledge systems.

Prerequisites:

- Canonical Meeting Knowledge and Knowledge Objects are implemented and stable.
- Durable knowledge is separated from meeting-specific actions and discussion
  history.

Out of scope:

- Building a full enterprise search product inside Meeting Lab.
- Treating raw transcripts or generated protocols as the knowledge source of
  truth.