Ship AI features that feel useful, not experimental.

Typically: problem framing, UX for the AI surface, prompt/system design, model integration, retrieval or tools where needed, evaluation, security considerations and production deployment. Scope is defined around the feature you need to ship, not a generic “AI project.”

We start with the simplest approach that hits quality targets, often a strong hosted model plus good retrieval and prompts. Fine-tuning or specialised models come in when evaluation shows they are worth the cost and complexity.

Yes. We design data flows with least-privilege access, clear retention boundaries and provider choices that match your security requirements. Private knowledge stays in retrieval pipelines you control rather than being casually pasted into prompts.

Through grounding (RAG), constrained outputs, tool use for facts that should be looked up, evaluation against real examples and UX that shows uncertainty or sources. No stack eliminates risk entirely. Good product design makes failure modes visible and recoverable.

A focused prototype can land in weeks. A production feature with retrieval, evaluation and polished UX usually takes longer and should ship in phases. Discovery clarifies data readiness and risk before estimates firm up.

Absolutely. Most engagements extend current web or mobile apps with a new AI capability such as assistant, search, automation or extraction, rather than rebuilding everything from scratch.

We are provider-pragmatic: OpenAI, Anthropic, Google and other APIs, plus open models when they fit latency, cost or data constraints. The product requirements drive the choice, not a preferred logo.