Industry
Data engineering and analytics platforms — ingestion, transformation, warehousing, streaming and the quality controls that make reporting defensible.
A dashboard that is 95% right is worse than no dashboard, because people act on it and then stop believing it. Our first move on data engagements is usually to make correctness observable: reconciliation against source systems, freshness metrics, and explicit alerting on silent failure.
Once the numbers are trusted, throughput and cost work becomes worth doing.
Case studies
Problem, constraint, decision, consequence. No vanity metrics, no logos we are not allowed to name.
All case studiesSample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.
A nightly batch pipeline that had outgrown its window, re-architected into an incremental streaming system with correctness checks that alert before customers notice.
Document intake and support triage moved to a draft-first AI workflow with human approval, measured against an evaluation set built from two years of historical cases.
Start a conversation
Tell us what you are trying to build, fix or decide. If we are not the right team for it, we will say so and point you somewhere better.