AI fails on bad data. We engineer the layer underneath it.

Warehouse to lakehouse, without the big bang

Move Teradata, Netezza, Oracle or SQL Server warehouses to a lakehouse without a big-bang cutover. Every table is reconciled row-for-row in a parallel run before the old system is switched off.

• ▸Automated code conversion for SQL, stored procedures and ETL jobs
• ▸Parallel run with reconciled row counts and checksums
• ▸Legacy licence retired on a dated plan

Typical engagement

4–6 weeks

to the first migrated domain in production

• ▸Pilot wave reconciled row-for-row in a parallel run
• ▸Wave plan with a dated licence decommission
• ▸Senior engineers only

Platforms built like software

We build Databricks, Snowflake and Fabric platforms the way software teams build products: infrastructure as code, CI/CD for pipelines, tested transformations. Your team inherits something they can run.

• ▸Terraform-managed workspaces, networking and access
• ▸CI/CD and automated tests for every pipeline
• ▸Runbooks and on-call handover to your team

Typical engagement

4–6 weeks

to a production-ready foundation

• ▸Environments, identity and networking in Terraform
• ▸CI/CD and tests on every pipeline
• ▸2–5 senior engineers plus an architect

Data your models can trust

GenAI, RAG and agents need governed, current, retrievable data — not a copy of SharePoint in a vector store. We build the pipelines, chunking, embeddings and access controls that keep answers correct.

• ▸Document and table pipelines with freshness SLAs
• ▸Retrieval evaluated against a labelled test set
• ▸Row- and document-level permissions enforced at query time

Typical engagement

4–6 weeks

to the first governed use case in production

• ▸Permissions enforced at query time
• ▸Answer quality scored against a labelled test set
• ▸Cost model per query

Catch bad data before your board does

Data contracts, automated tests and lineage catch bad data before it reaches a dashboard, a regulator or a model. Access policies live in code, so audits take days instead of months.

• ▸Data contracts between producers and consumers
• ▸Quality tests with alerting on every critical table
• ▸Column-level lineage and access policies in Unity Catalog or Horizon

Typical engagement

2 weeks

to a register of critical data and owners

• ▸Contracts and tests on priority domains
• ▸Column-level lineage in your catalog
• ▸Audit evidence pack template

Pay for the workloads you run

Most platforms carry spend that no workload needs: idle clusters, oversized warehouses, full refreshes that could be incremental. We find it, remove it and put guardrails in place so it stays gone.

• ▸Cost baseline by team, pipeline and query
• ▸Right-sized compute and incremental processing
• ▸Budgets and alerts that stop regressions

Typical engagement

1–2 weeks

to a cost baseline by team and pipeline

• ▸Ranked savings backlog with estimated value
• ▸Before-and-after measurement on every fix
• ▸Budgets and alerts so savings stick