ADR-011: Four-Layer Data Architecture — Raw Archive → Knowledge → Ontology → Decision

- Layer 1: ArchiveArtifact — immutable original with retention policy
- Layer 2: KnowledgeArtifact — extracted knowledge (observations,
  segmentations, feature vectors, relations)
- Layer 3: Knowledge Graph / Ontology (documented, not implemented)
- Layer 4: Decision Intelligence (existing DecisionCase)
- DataLifecycle: tracks every step with artifact lineage
- Key principle: AI models trained on curated datasets, not whole archive
- Ontology answers 'what does it mean in our domain?'

Long-term goal: Every observation converted once to structured
knowledge, reused infinitely for analysis, decisions, training.

Next: PR-005A — Minimal Mission Import UI for MVP-0

docs/design/adr/ADR-011-Four-Layer-Data-Architecture.md

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+# ADR-011: Four-Layer Data Architecture
+
+## Status
+Accepted
+
+## Context
+We need a clear separation between raw data, extracted knowledge, and decision intelligence to avoid building expensive, tightly-coupled AI systems.
+
+## Decision
+Separate into four layers:
+
+### Layer 1: Raw Archive (cheap, immutable)
+- Original files never changed
+- Hash guarantees integrity
+- Storage policy: active (30 days) → archive (long-term, cheaper)
+
+### Layer 2: Knowledge Extraction
+- Objects, geometry, GPS, classifications, bounding boxes, embeddings
+- Observations and Evidence created
+- Structured knowledge used for most queries, not video files
+
+### Layer 3: Knowledge Graph / Ontology
+- Road A12 → has Observation → Crack → belongs to Area → Stockholm
+- Answers: "Show all main roads where cracking increased last 12 months"
+- Comes from knowledge graph, not by reading video files
+
+### Layer 4: Decision Intelligence
+- Verified Decision Cases
+- Curated datasets for AI training
+- Business impact tracking
+
+## Key Principles
+
+1. **Archive Artifact** = Original file, never modified
+2. **Knowledge Artifact** = Extracted knowledge, can be re-generated
+3. **Ontology before model** — taxonomy answers "what does it mean in our domain?"
+4. **AI models trained on curated datasets**, not whole archive
+
+## Data Lifecycle
+
+```
+Upload → Immutable Archive → Metadata Extraction → Knowledge Extraction
+→ Ontology Mapping → Decision Pipeline → Learning → Archive Retention
+```
+
+Each step produces a new Artifact with version history.
+
+## Long-term Goal
+
+"Every real observation is converted once to structured knowledge and can then be reused infinitely for analysis, decision support, history, training, and future AI models."
+
+## Consequences
+
+### Positive
+- Raw data = revisionable archive
+- Real product = growing knowledge base + verified Decision Cases
+- Can swap AI models without losing originals
+- Cheaper storage (archive vs active)
+
+### Negative
+- More complex pipeline
+- Need to manage extraction models
+- Knowledge graph maintenance
+
+## Related
+- ADR-002: Artifact is common base contract
+- ADR-003: Event Sourcing for traceability