LLMs and ML built into real workflows — measured by business outcome, not demo wow.
We’ve shipped LLMs into production for lenders, hospitals and retailers since 2022. The work is mostly unglamorous — evals, retrieval quality, guardrails, cost — and it’s exactly the work that decides whether you ship or get stuck in pilot.
The work, named.
RAG with evals: golden sets, regression tests, A/B harnesses — not vibes
Fine-tunes and small-model deployment for cost and latency
Guardrails: PII redaction, prompt-injection defences, output filtering
Document understanding, support deflection, sales intelligence patterns
Audit logs and reviewable prompts your compliance team can sign off
A few things you can’t order off a menu.
Engineers who’ve shipped LLM features in regulated industries
We’ll talk you out of a model when retrieval or rules are the actual answer
Multi-provider fluent: Anthropic, OpenAI, Gemini, open-weights
Common questions, straight answers.
Can you build on Bedrock / Vertex / Azure OpenAI?+
Yes — and we have a recommendation for which one based on your compliance posture, data residency and the model behaviour you need.
How do you measure quality?+
Task-specific eval harnesses run on every PR, with a curated golden set per task and human review on regressions.
Do you do data labelling and fine-tuning?+
Labelling we partner for; fine-tuning, including LoRA / QLoRA / DPO, we do in-house when it’s the right tool.
Have a problem worth solving? Bring it.
Tell us what you're trying to ship. We'll come back inside two working days with people, a plan, or both.