Spatial processing depends on laptops, shared folders, or scripts only one person understands.
AI-Ready Spatial Data Infrastructure & Engineering
AI-Ready Geospatial Data Infrastructure, PostGIS Pipelines & Feature Stores
Cloud-native spatial data engineering built specifically for AI, satellite pipelines, and real-time analytics. High-throughput ETL, PostGIS feature stores, STAC ingestion, and sub-50ms vector tile APIs.
// The buying trigger
You probably need this when…
CRS, geometry, lineage, and temporal issues surface after data reaches users.
Raster and vector workloads are expensive, slow, and invisible to platform observability.
// Concrete scope
What the engagement delivers
Every item is tied to an acceptance owner and evidence. Final scope is confirmed after data, security, and integration review.
Source-to-consumer architecture and data contracts
Batch or streaming ingestion with spatial validation
Tested transformation, tiling, indexing, and publication pipelines
Orchestration, CI/CD, lineage, alerts, and cost observability
Runbooks, ownership model, and handover
// Delivery process
From evidence to operational handover
- 01
Profile
Measure quality, volume, velocity, coordinate systems, and consumer SLAs.
- 02
Contract
Define schemas, validity, freshness, lineage, and failure behavior.
- 03
Engineer
Build idempotent pipelines and storage for the access pattern.
- 04
Operate
Add tests, observability, cost controls, and ownership.
// Proof before promises
How value is verified
No vanity accuracy number. Evidence is chosen around the operational decision and agreed before delivery starts.
- ✓Freshness, completeness, geometry validity, and duplicate-rate service levels
- ✓Replay and recovery demonstration at representative volume
- ✓Measured throughput, runtime, and cost per processing unit
// Buyer questions
Frequently asked questions
Which cloud and tools do you support?+
AWS, Azure, and GCP with Airflow, dbt, Spark, GDAL, PostGIS, object storage, and cloud warehouses.
Can you modernize an existing pipeline?+
Yes. We identify what to retain, instrument, rewrite, or retire.
Do you handle raster and vector?+
Yes, including imagery, elevation, point clouds, features, networks, and derived tiles.
What data formats and tile standards do you build for?+
We build automated pipelines for Cloud Optimized GeoTIFFs (COG), PMTiles, FlatGeobuf, GeoParquet, vector tiles (MVT), and GeoJSON, orchestrated via Apache Airflow, Dagster, dbt, or AWS Step Functions.
How do you prevent spatial data leakage and coordinate reference system (CRS) errors?+
We implement automated CI/CD spatial validation gates that enforce CRS standardization (e.g. EPSG:4326 / EPSG:3857 or local UTM projections), detect invalid geometries via ST_IsValidReason, and enforce data contracts before records reach downstream analytics.
Can pipelines scale elastically with satellite data volumes?+
Yes. We design cloud-native serverless or containerized compute clusters (AWS Batch, ECS, Google Cloud Run) that scale to hundreds of parallel workers during heavy image ingestion and scale down to zero when idle.
// Related expertise
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Bring us the problem, not a perfect brief.
Three fields start the conversation. An engineer will help determine fit, data readiness, and the smallest useful next step.
Book a Strategy Call →