Pattern of the week: "clean seams" beats clever models, every time.
We're seeing the same anti-pattern across health, agri and logistics deployments:
- Team picks a powerful model.
- Wires it to messy upstream data.
- Wraps it in a clever orchestration layer.
- Demos brilliantly.
- Dies on contact with real operations.
The failure isn't the model. It's the seam between AI output and system of record. If your data fabric can't carry settlement-grade truth (who, where, when, what changed, who signed off), no amount of model capability will save the workflow downstream.
The c51 / REALM framing we keep coming back to:
1. Identity layer (who is this entity, persistently)
2. Provenance layer (where did this data come from)
3. Settlement layer (what's the source of truth when things disagree)
4. Orchestration layer (only then does AI go on top)
Most "AI strategies" we audit skip 1-3 entirely. The interesting work right now is the boring layer underneath.
If you're architecting at this layer - country platforms, RWD/RWE, smallholder data fabrics, wearable to EHR - DM open. Happy to compare patterns.
#AIOrchestration #DataFabric #SettlementGradeData #REALM