AI Systems
AI creates value when it ships inside a system that people rely on, with the data architecture, evaluation and operational discipline that reliability demands. We build AI as production software, not as demos.
The work we undertake
Production AI implementation
Language model and machine learning capabilities integrated into real products and workflows, with evaluation, guardrails and observability.
AI enabled products and workflows
Redesigning a workflow around what AI makes possible: assistance, drafting, classification, and decision support with a human in control.
Retrieval, search and intelligence systems
Retrieval augmented generation, semantic search and internal knowledge systems grounded in an organization's own data.
Automation and AI integrations
Connecting AI capability to the systems where work actually happens, so automation lands in the process rather than beside it.
Data and application architecture
The pipelines, storage and application structure that AI systems stand on. The model is a component; the system is the product.
Where this capability stops
We do not sell AI strategy decks or run innovation theatre. If a problem does not need AI, we say so and build the simpler system.
Held to our own standard
Our AI work ships inside operating software with real users, and is held to the same production standard as everything else we run.