A proprietary reference model of what a city of a given type should monitor

Continuous classification of every gap: never-collected, stale, siloed, low-quality or spatially patchy

Plain-language explanations of each gap for non-technical officials

Equity-of-coverage mapping that exposes under-monitored areas

A standing, dated record of what the city knew and when

Outputs that distinguish a cheap integration fix from an expensive new build

Ingest, reconcile and re-ground a model as the city actually changes

Cadence matched to each signal's real rate of change — traffic in seconds, zoning in months

Knowledge graph plus vector index to represent uncertainty, not overwrite it

Temporal decay modelling — every fact carries a notion of staleness

Anomaly detection that separates a transient spike from structural change

Resilience framing: where is the city blind if a sensor network fails?

Cross-reference datasets against the reference model and flag discrepancies

Chase provenance across departmental silos

Compose evidence and procurement-ready outputs in the funder's own format

Cost-of-blindness modelling that turns a coverage map into a business case

Cross-city benchmarking — anonymised, comparable coverage across peers

Private AI workspaces for public-sector and infrastructure organisations