Data infrastructure & AI that actually works
Your current data infrastructure is probably broken. Most companies have data scattered across systems that don't talk to each other, "AI initiatives" that never make it to production, and automation that creates more work than it eliminates.
- Data infrastructure that actually processes what you need, when you need it.
- ML models that deploy to production and stay there.
- Automation that eliminates manual work instead of creating busy work.
We don't build proof-of-concepts or "AI strategies." We build production systems that generate measurable business value. No consultant PowerPoints, no 6-month "discovery phases" - just working software.
Why most data projects fail
- Wrong problem: Building AI before fixing data quality. You can't train models on garbage data.
- Wrong team: Hiring data scientists to build infrastructure. You need engineers who understand production systems.
- Wrong timeline: Expecting results in 3 months from systems that should have been built 3 years ago.
- Wrong vendors: Outsourcing to consultants who've never deployed anything to production. We write every line of code ourselves.