Enterprise AI Applications
An assistant that answers from your own policies, contracts, or technical documentation, and only shows each user what they are allowed to see.
How we approach itData & Analytics Engineering
One number for revenue, not three.
We design and build data platforms on Databricks, AWS and Azure, starting from the definitions people argue about. Data contracts, tested pipelines, and a named owner for every dataset.
The problem
What we usually find.
Finance and sales report different revenue figures, and both are "right" by their own definition. A pipeline fails at 3 a.m., is rerun, and quietly double-counts a day of orders. Moving to a new platform does not fix either problem. Agreeing on definitions and making reruns safe does.
What we do
Services.
- Assessment of the current platform: sources, consumers, costs, and failure points
- Lakehouse design and incremental migration from legacy warehouses
- Batch and streaming pipelines that can be rerun without duplicating data
- Data contracts, quality checks, and lineage that people actually read
What you get
Deliverables.
- An inventory of sources and consumers, with the disputed definitions listed explicitly
- Data contracts for the first critical datasets, agreed by producer and consumer
- Pipelines in version control with tests and CI, deployable by your team
- Reconciliation checks against the old system for the migration period
Reruns that do not double-count
Every dataset we deliver has a stable key, a documented replay procedure, and a reconciliation check. Analytics and AI work then build on the same tables instead of their own extracts.
DatabricksDelta Lakedbt & SQLAWS & Azure
Typical engagements
Where this usually starts.
Data Platform Modernization
Replacing an ageing warehouse or a tangle of scripts with a lakehouse, without a big-bang cut-over.
How we approach itContact
Planning a data platform migration?
Tell us what runs today and what keeps breaking. We usually start with one dataset that matters and prove the pattern there first.