Services
Data engineering for AI that can be evaluated.
We build the data foundations — ingestion, labeling workflows, feature paths, and eval sets — that production AI depends on.
Evidence note: Data engineering offerings are described as capabilities until project-specific evidence is approved for publication.
Problems we address
Unusable training data
No versioning, no splits, no quality bar.
Capabilities
Pipelines
Ingestion, transformation, and validation for ML/AI workloads.
Eval corpora
Held-out sets and scenario libraries tied to acceptance criteria.
How engagements typically run
Audit
Sources, quality, privacy.
Design
Schemas, ownership, refresh cadence.
Build
Pipelines and quality checks.
Serve AI
Connect to training, RAG, or monitoring.
Related proof & next steps
Related services
Questions
Ingestion and validation for ML/AI workloads, labeling workflows, versioned splits, and evaluation corpora tied to acceptance criteria.
Talk with us about data engineering.
Share constraints and goals — we will respond with a technical discovery path.