Data engineering services with AI

Data Strategy and Consulting

We assess your current estate, define the target architecture, and produce a roadmap that sequences the work by business value rather than by technical tidiness.

Data Architecture Design

Warehouse, lakehouse or hybrid — we design for the queries you actually run, the latency you actually need and the governance you are actually held to.

Data Pipeline Engineering

Batch and streaming pipelines with proper orchestration, retries and observability, so a failed load is something you know about before your users do.

Data Quality and Validation

Automated validation, anomaly detection and lineage tracking. Trust in a dataset is built in the pipeline, not asserted in a meeting.

Data Governance

Access control, retention, classification and audit trails designed in from the start, so compliance is evidenced rather than reconstructed.

Data Migration and Modernization

Phased migration off legacy platforms with parallel running and reconciliation, so nothing is cut over on faith.

Feature Engineering for ML

The unglamorous work that decides model accuracy — feature stores, transformations and reproducible training datasets.

Real-time Data Processing

Event-driven architectures for the cases where a decision made an hour late is a decision not worth making.

MLOps and Model Infrastructure

CI/CD for models, versioning, monitoring and rollback — the plumbing that makes deployed ML maintainable rather than fragile.