Native support for your EO / GIS stack
An observability layer for production EO/GIS systems. It deploys inside your environment, validates your geospatial outputs, traces failures to their root cause, from obvious errors to silent ones, and produces audit-ready quality evidence.
Every system can report success while the product is quietly wrong. These are a few of the silent failures generic monitoring never sees.
A WMS returns 200, but the tile is empty.
A raster is valid, but the CRS shifted.
A pipeline finishes, but NoData contaminated the output.
A scene is fresh, but the derived index is outside its expected range.
A report is generated, but the evidence chain is not audit-ready.
A job succeeded, but the data falls outside the requested AOI.
A STAC item is valid, but the linked asset is missing or its CRS metadata lies.
A netCDF/Zarr write succeeds, but a chunk is silently all-NaN.
The dashboard stays green while the customer report is already wrong.
Spheroid flags the broken scene and hands you the evidence before it reaches a decision.
Four capabilities on one read-only path: detect, monitor, explain and score every geospatial output where it's produced.
Spheroid ingests logs and metrics from every component and opens your rasters, cubes and vector tables read-only, parsing the payload itself, so a flaw is caught the moment it's written.
Reads: logs · metrics · Raster · netCDF · PostGIS · STAC
A green health check means the service responded, not that the layer it served is usable. Spheroid measures delivery the way a downstream user actually experiences it.
SLA: delivery quality, not just uptime
Geospatial AI agents connect incidents to lineage, provenance and stack evidence. Read-only and human-approved.
Evidence: Lineage · RCA · Provenance
Continuous, standards-aware quality scorecards keep EO/GIS outputs usable release after release.
Frameworks: ISO 19157 · CARD4L · QA4EO
Infrastructure and table monitors can't open a raster or a data cube. Spheroid parses and validates EO/GIS artifacts where they live.
| Capability | System APM e.g. New Relic, Datadog | Data observability e.g. Monte Carlo, Bigeye | Spheroid EO/GIS-native |
|---|---|---|---|
| Object inspected | Hosts, services, logs, APIs. | Tables, warehouses, rows. | Pixels, variables, geometries, spatial metadata. |
| Payload understanding | None — files are blind blobs. | None — can't open binary formats. | Native parsing of COG, netCDF, Zarr, GRIB, PostGIS, STAC. |
| Spatial integrity | Endpoint uptime only. | Row checks; misses geometry. | CRS, AOI/BBox drift, PostGIS validity. |
| Root cause | Microservice traces. | SQL column lineage. | Agents trace scene → collection → service. |
| Standards | DORA, SLAs, golden signals. | Custom rules, freshness. | ISO 19157, CARD4L, QA4EO, INSPIRE. |
| Deployment | Cloud SaaS, multi-tenant. | Cloud SaaS, third-party LLMs. | On-premise / air-gapped, zero egress. |
GeoTrace is a domain-trained AI agent for geospatial systems. It reasons over CRS, gaps, geometry and lineage, traces the failure to its exact source, and turns it into evidence. Deterministic-first, with a human approving every action.
EO and GIS outputs now drive reporting, operations and decisions — they need more than job status and infrastructure metrics.
Spheroid gives engineers and managers one shared view of product quality, service reliability and downstream trust.
Catch broken CRS, NoData, fill-values and geometries before they reach a product — the same rigour IT gets for its apps.
Real WMS/OGC availability, GetMap latency and empty-tile SLOs — delivery signals beyond “the job finished”.
Standards-aware scorecards (ISO 19157 · CARD4L · QA4EO) that make downstream trust auditable, release after release.
