SERVICES · DATA & PLATFORMS
The data foundation that makes AI dependable.
Sixty to seventy percent of every AI project is data work. We build the pipelines, stores and runtime so the model layer has something true to stand on — and we run it in your cloud or on your metal.
What done looks like.
- Governed pipelines with contracts and freshness SLAs
- Unstructured ingestion for documents, email and images
- Lakehouse/warehouse foundation
- Vector and graph stores
- Model serving, gateways and cost control
- Private or air-gapped deployment
Platform engineering.
The application and infrastructure work that sits under the data and model layers.
- Microservices and serverless architectures
- Data migration and legacy modernisation
- Cloud-native builds
- API management
- Multi-cloud deployment across AWS, Azure and GCP
Stack.
- Pipelines
- dbt · Airflow · Dagster · Kafka · Debezium · Fivetran
- Storage
- Databricks · Snowflake · BigQuery · Fabric · PostgreSQL
- Quality
- Great Expectations · Monte Carlo · Soda
- Serving
- vLLM · LiteLLM · Portkey · Bedrock · Azure AI Foundry · Vertex
- Cloud
- AWS · Azure · GCP · Kubernetes
PROOF
We build our own product this way.
- Zero-loss ingestion (S3 webhook catcher, watermark replay, poison quarantine)
- Identity unification across tools
- As-lived historical replay
- Postgres-native graph with evidence refs