Services
Data, Analytics & AI
We build the unglamorous half first: reliable ingestion, a warehouse with a schema someone can reason about, and numbers that reconcile with the source system. Machine learning gets added when there is a decision it demonstrably improves, and not before.
What you receive
- Ingestion pipelines with data quality checks and freshness monitoring
- A warehouse schema documented for the analysts who will query it
- Dashboards scoped to specific decisions, not generic metric walls
- Applied ML models with a stated baseline they are measured against
This is a good fit when
- Different teams report different numbers for the same question
- Reporting depends on someone exporting a CSV each month
- You want to know whether an ML approach is worth it before committing to one
Other services
All servicesTell us what you're trying to build
Send us the problem, not a specification. We'll tell you honestly whether we're the right people for it, and if we aren't, we'll say so.