AI & Data Engineering

Yes. That's what retrieval-augmented generation (RAG) is for. We connect the model to your own content: docs, tickets, product data, knowledge base, so answers are grounded in your sources rather than the model's general training, and we cite where each answer came from so you can check it. The hard part is rarely the model; it's getting your data clean, chunked and permission-aware, which is where most of the engineering goes.

We treat AI like any other production system: measured, not assumed. Before launch we build an evaluation set that scores answers against known-good cases, and once live we monitor quality, cost and latency, with logging and alerts when behaviour drifts. That way a regression from a new model version or a change in your data shows up on a dashboard, not in an angry customer email.

Honestly, for many teams the data needs work first, and we'll tell you if it does rather than bolting AI onto a shaky foundation. We can assess what you have, sort out the pipelines, structure and access controls, then build the data layer the AI actually depends on. Sometimes the most valuable thing we ship in the first phase isn't a model at all; it's a clean, reliable foundation that makes everything after it possible.

We default to retrieval over your own data, so answers are grounded in your sources and cite where they came from instead of being generated from the model's memory. Before launch we build evals on real cases with acceptance thresholds, so we measure answer quality rather than hoping it's good, and we add input and output guardrails plus a human-owned fallback for when the model is unsure. Where we can't reach a quality bar we can stand behind, we'll tell you the feature isn't ready rather than ship a confident guess.

No lock-in by design. We treat the model as a swappable component behind your own retrieval layer and structured-output contracts, and we work across providers like OpenAI, Anthropic, Google Gemini, Azure OpenAI and AWS Bedrock so you can move if pricing, latency or policy changes. You own the same things you own everywhere else with us: the code, the prompts, the eval suite and the retrieval index over your data, handed over with documentation, so the feature isn't dependent on us or on a single vendor to keep running.

By default, no. We pseudonymise or abstract where needed and follow per-client rules about what may or may not leave your environment. For clients with stricter requirements, we can use self-hosted or tenant-isolated AI options, defined together during onboarding.