- Status: STRUCTURE FROZEN (not LOCKED/FINAL)
- Frozen: field names, semantics, relationships
- Not frozen: implementation, algorithms, confidence calculation
- Three tests executed and PASSED:
1. Decision Invariance Test — all 8 scenarios use same 7-field structure
2. Evidence Variation Test — Decision Object identical regardless of evidence type
3. Explainability Invariance Test — chain Reality→Observation→Evidence→Finding→Decision
works for all actionable decisions
- Results summary:
- Manual Review: 6 PASS, 2 OBSERVATION, 0 FAIL
- Decision Invariance: PASS
- Evidence Variation: PASS
- Explainability Invariance: PASS
- Decision Quality Gate: PASS
- Verb Rule: PASS
- Overall: VALIDATED FOR FIELD TRIALS
- Internal validation complete
- Ready for real customer testing
- Not production truth yet
- Open questions documented (not added as fields):
- Observation A: Cost estimate for investment decisions
- Observation B: Explicit low confidence communication
- Next: Three real customer cases (municipality, property owner, contractor)
Rationale: Freeze structure before testing, run tests against locked model,
mark as 'Field Trial Ready' not 'Final'. Model changes when real data
contradicts it, not before.
- Added Step 7: Learning (feedback loop from Business Impact to Intelligence)
- Control Intelligence definition: LandveX produces Control Intelligence, not AI
- Three target customer cases defined:
1. Municipality — Inspect or wait? (Maintenance prioritization)
2. Property Owner — Repair now or plan later? (Cost vs risk)
3. Contractor/Operations — Which action first? (Operational planning)
- Validation requirements: Run each case through full pipeline, document breaks,
revise only after data contradicts model
- Communication principle: Observation → Analysis → Recommendation
(AI is implementation, recommendation is product)
Rationale: Stop modeling, start observing. Model changes when data contradicts
it, not before. Three real customer cases before freezing.
- Six layers (added Evidence between Observation and Finding):
1. Reality
2. Observation
3. Evidence (linked observations with context)
4. Finding
5. Decision
6. Business Impact
- Decision Object restructured:
1. Decision — what should user decide?
2. Why — why system recommends this
3. Evidence — what observations support this
4. Confidence — how certain (3 dimensions)
5. Consequence — what if nothing done
6. Action — next step
7. Business Impact — economic/operational meaning
- Confidence Model (3 dimensions):
- Observation Confidence: how certain is detection?
- Evidence Strength: how strongly supported?
- Recommendation Confidence: how certain is recommendation?
- Explainability Principle:
- Every Decision Card must be explorable
- User can click: Decision → Finding → Evidence → Observations → Reality
- Competitive advantage: traceability to source material
- Business Impact Model (4 dimensions):
- Risk, Cost, Time, Opportunity
- Three validation scenarios:
1. Road Crack — simple, common
2. Damaged Facade — complex, critical
3. Broken Road Sign — simple, regulatory
- Pass criteria: Same Decision Object works for all three
Rationale: Decision Intelligence, not BI. Evidence-backed decisions
are the core product. Explainability is competitive advantage.
Validation against real scenarios before freezing.
Bernt