We build AI products that solve real problems for real users — using foundation models from OpenAI and Anthropic, plus custom pipelines when general-purpose isn't enough.
An AI demo and an AI product are different things. We build the second one — with evaluation, fallbacks, cost ceilings, and the unglamorous parts that make it usable on day 100.
Chat, summarisation, extraction, classification — built into existing products with proper streaming, retry, and rate-limit handling. No magic, just reliable behavior.
Multi-step agents that call tools, read data, and take action. Bounded, observable, and rolled back when something goes wrong.
Real-time voice (STT → LLM → TTS) and multimodal (image, document) pipelines. Sub-second response when latency matters.
When foundation models don't fit — fine-tuning, retrieval (RAG), embeddings, and small bespoke models. Trained on your data, hosted on your infrastructure.
Most AI projects ship a demo and stall on edge cases. We design the evaluation harness in week one — so by the time the feature is in front of users, we already know what it gets wrong.
What is the model deciding, and how do we know it's right? Concrete examples, edge cases, and a written specification — before any prompt is written.
A test suite that scores model outputs on real examples. Runs on every change. Without this, you're guessing.
Prompt engineering, retrieval, fine-tuning — whichever moves the eval score. Decisions tied to numbers, not hunches.
Streaming, caching, fallbacks, cost ceilings, and observability. The boring parts that decide whether the feature survives in production.