AI
Plenty of AI projects stall at the prototype. We build LLM-powered features into your actual product — grounded in your data, monitored, cost-aware and running on infrastructure you control.
// what we do
Chat assistants, summarisation, classification and data extraction built into your app — on Bedrock, a single provider, or many models through OpenRouter.
Answer questions over your docs, tickets or knowledge base — with the sources cited.
Agents that handle repetitive workflows by calling your tools and APIs, with guardrails.
Take an existing AI prototype and make it reliable, secure, observable and affordable.
// packages
Clear scope and timeline up front. Every project is quoted after a short conversation about your setup.
One AI feature — a chatbot, document search or automation — built into your product end to end.
Need something different? Tell us what you need and we’ll suggest a scope.
// from the blog
Evaluations, guardrails, prompt injection, cost limits, observability and fallbacks — what an LLM feature needs before real users rely on it.
Why most retrieval-augmented generation demos disappoint in production, and the chunking, retrieval and evaluation practices that fix them.
Tell us a bit about your product and what you’re trying to get done. You’ll hear back from an engineer, not a sales team — no obligation.