AI RAXBAR has moved from an agent demo toward a canonical operational data foundation
Since the hackathon submission, I have continued developing AI RAXBAR as a real operational decision-support platform for electricity distribution.
A major focus has been separating source facts, reported outcomes, matching decisions, verification, and AI interpretation so that the system does not turn missing or ambiguous evidence into false operational conclusions.
This week, the project reached an important engineering milestone: Canonical Core V1 was committed and independently validated in PostgreSQL.
The validated baseline contains:
- 8,082 immutable source records
- 1,015 transformer assets
- 1,015 passport revisions
- 3,757 operational observations
- 3,310 completion reports
- 3,757 explicit match decisions
- 519 evidence-supported observation/report links
Source identity, payload fingerprints, capture lineage, schema constraints, and migration state were independently checked after commit. The validation completed with 62/62 existing tests passing and no source mutation.
One of the most important changes is semantic rather than technical. AI RAXBAR now explicitly preserves uncertainty:
- SUPPORTED_CLOSURE
- AMBIGUOUS_MATCH
- IDENTITY_UNRESOLVED
- COVERAGE_INCOMPLETE
Instead of treating the absence of a matched completion report as proof that work was not completed, the platform keeps unknown and ambiguous states visible.
This follows the principle behind the original hackathon Agent:
AI does not own the truth. Evidence does.
The next engineering stage is connecting this frozen canonical core to consistent read models for Daily, Weekly, Monthly, Network, Brigade KPI, TP Risk, Territory/Health, GIS, and planning views — so that every operational screen derives its conclusions from the same evidence model.
The goal remains the same: build an AI-assisted operational system where reasoning is useful, uncertainty is explicit, high-impact decisions remain accountable, and every conclusion can be traced back to evidence.
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