Five layers, one accountable pipeline.
Streaming and batch ingestion across satellite imagery, fiber DAS interrogators, IoT/SCADA telemetry, GIS feeds, and operator APIs.
- ·REST, gRPC, MQTT, Kafka
- ·Edge buffering for intermittent links
- ·Schema registry per source
Deterministic rule checks plus ML anomaly detection. Every record carries lineage, confidence, and provenance metadata.
- ·Schema + business-rule validation
- ·Outlier and drift detection
- ·Full lineage and audit trail
Geospatial and temporal alignment of heterogeneous sources into a single operational picture.
- ·Spatial joins on H3 / S2 grids
- ·Time alignment with skew correction
- ·Entity resolution across feeds
Event-driven workflows that turn validated signal into NOC alerts, work orders, or downstream actions.
- ·Rule + model triggers
- ·Human-in-the-loop checkpoints
- ·Replayable workflow runs
Designed to plug into existing C2, OSS/BSS, GIS, ITSM, and autonomy platforms — not replace them.
- ·REST + streaming egress
- ·SNMP, ServiceNow, ArcGIS adapters
- ·SSO, RBAC, audit hooks
What a validated record looks like.
{
"ingested_at": "2026-05-03T14:32:01Z",
"source": "fiber_das.trunk_a",
"segment": "TRUNK-A-32.4km",
"validation": {
"schema": "pass",
"rules": "pass",
"anomaly_score": 0.07
},
"fused": {
"geo": { "lat": 38.9072, "lng": -77.0369, "h3": "8a2a1072b59ffff" },
"co_signals": ["weather.noaa", "permits.dc.gov"]
},
"action": {
"workflow": "noc.dispatch.v3",
"status": "queued",
"operator_review_required": false
}
}Every record passing through the pipeline carries its validation result, anomaly score, geospatial fusion context, and the workflow that consumed it.
That structure is what makes downstream automation safe: nothing acts on a signal whose provenance can't be traced back to an ingest source and a validation outcome.
Schema is illustrative. Real deployments are scoped to the customer's data contracts.