Platforms and pipelines
Ingestion, transformation, and orchestration on the cloud you already pay for — Azure, AWS, and the warehouse you can staff.
Data
Data engineering
AI is only as good as the pipes underneath it.
Pipelines, lakehouse, contracts, and serving layers that make analytics and models trustworthy. We treat data as a product with owners, SLAs, and lineage.
What you leave with
- Reliable pipelines with lineage, quality tests, and on-call
- A serving layer fit for BI, features, and retrieval-augmented systems
- Contracts between producers and consumers so schema drift does not sink the roadmap
How we typically engage
Data for AI
Chunking, embeddings, feature stores, and eval datasets. The unsexy work that makes agents and copilots accurate.
Governance that ships
Catalogues, access, retention, and APP-aligned handling — designed so teams can still move.