What we do
Data & AI
Analytics, machine learning, and GenAI — grounded in a trustworthy data foundation.
Overview
We turn scattered data into a single source of truth, then put it to work with analytics, machine learning, and production-grade GenAI.
Every model ships with evaluation, observability, and citations — so you know whether it's actually working.
Typical stack
What's included
- Cloud data warehouses and ELT pipelines
- Semantic layers and self-serve analytics
- RAG and agentic GenAI systems
- Model fine-tuning and evaluation harnesses
- MLOps: cost, latency, and quality dashboards
Outcomes
What you'll gain
A single, trustworthy source of truth across your systems.
Production GenAI that's grounded, evaluated, and observable.
Faster, self-serve answers for business teams — without SQL.
Clear visibility into model cost, latency, and quality.
How we work
A delivery process you can see
Discover
We map your goals, constraints, and systems — then agree on what success looks like and how we'll measure it.
Design
Architecture, scope, and a delivery plan you can see. No black boxes — you know what's shipping and when.
Build
Senior teams ship in short iterations with a working demo every sprint, so you steer as we go.
Operate
We harden, document, and hand over — or stay on to run and evolve it alongside your team.
Related work
Data & AI in action
FAQ
Common questions
Is our data safe with GenAI?
Yes. We deploy on your infrastructure where needed, restrict what models can access, and add audit logging and access controls by default.
RAG or fine-tuning?
Usually RAG first — it's cheaper, citable, and easy to update. We fine-tune only when the task genuinely needs it, and we measure the difference.
How do you know the AI actually works?
Every model ships with an evaluation harness and regression suite, so quality is measured continuously — not assumed.