Data & AI
From warehouse plumbing to production models.
Most AI pilots die in the gap between a demo and something your team relies on daily. We build the unglamorous parts first — clean data pipelines, a warehouse that answers questions correctly, evaluation that catches when a model gets worse — so the assistant or forecast on top of it is something people actually trust.
What's included
Data pipelines & warehousing
Ingestion, transformation and a warehouse schema that answers the questions your business actually asks, built so the next analyst can extend it without reverse-engineering your intent.
LLM tooling & RAG pipelines
Retrieval-augmented systems grounded in your actual documents and data, with evaluation and guardrails — not a chatbot that confidently invents answers.
Forecasting & analytics
Models built for the decision they inform, benchmarked against a naive baseline so you know they're actually earning their complexity.
Production ML operations
Monitoring for drift, a retraining path that doesn't require an engineer to babysit it, and rollback when a new model version underperforms.
Frequently asked
We already tried an AI pilot and it didn't go anywhere. What's different?
Usually the pilot answered a demo question well and a real question badly, because the data underneath wasn't ready and there was no evaluation loop to catch it. We build the data foundation and the evaluation harness before the model is the headline — it's less exciting in week one and it's why the thing survives contact with real users.
Do you build on a specific AI model provider?
Whichever fits the constraint that matters most — cost, latency, data residency, or capability. We're not tied to one vendor, and for a lot of use cases the right architecture matters far more than which model sits behind it.
How do you handle data privacy for AI systems trained on our internal data?
Retrieval-augmented approaches usually mean your data never leaves your infrastructure to train a model at all — it's retrieved at query time, not baked into weights. Where fine-tuning is the right call, we scope exactly what data touches which system before a line of code is written.
What does 'evaluation' actually mean for an LLM system?
A benchmark set of real questions with known-good answers, checked automatically every time we change a prompt, model, or pipeline step — so a change that improves one answer and quietly breaks another gets caught before your users find it.