Service

AI / ML Integration

The interesting question is rarely which model to use. It is what the feature does when the model is wrong, slow, or expensive — which is what separates an AI feature people rely on from a demo that impressed once.

What's included

How we help

LLM-powered product features

Assistants, summarisation, extraction, and generation wired into real workflows, with prompt versioning and evaluation rather than a string that was tuned once.

Retrieval-augmented generation

RAG pipelines over your own documents and data — chunking, embeddings, and vector search tuned so answers stay grounded in sources you control.

Semantic and intelligent search

Search that matches meaning rather than exact keywords, with hybrid ranking that keeps precise queries precise.

Recommendations and custom models

Recommendation engines and bespoke ML pipelines where an off-the-shelf API does not fit the problem, including training and serving infrastructure.

How we work

Our approach

Discovery separates the parts of the problem that need a model from the parts that do not. Design covers latency, cost, and failure behaviour alongside the happy path. Build ships behind evaluation harnesses. Launch & Grow monitors quality and spend as usage grows.

See the full process