Four kinds of work. One standard.

01

Production AI

Most AI stalls between a promising demo and something a business can trust. We engineer the systems that close that gap: retrieval, agents and LLM applications engineered with evaluation, guardrails and clear failure behaviour — including private and on-premise deployments where data cannot leave your walls.

• Enterprise LLM applications
• Agentic workflows
• Retrieval-augmented generation
• Private and on-premise AI
• Evaluation and guardrails

02

Data and machine learning

Models are only one part; the system around them is what makes them useful in production. We build the pipelines, serving, and monitoring that turn a data idea into something operational, and we keep it honest with drift and anomaly detection.

• Machine learning and forecasting
• Data engineering and pipelines
• MLOps and model serving
• Data platforms and warehouses
• Anomaly and drift detection

03

Custom software engineering

AI is one tool in modern software engineering, not a replacement for it. We build the backends, APIs and distributed systems underneath — and modernize systems that have stopped scaling — with the same production discipline.

• Distributed and backend systems
• APIs and integrations
• Cloud-native platforms
• Performance and scaling
• Modernization of legacy systems

04

Architecture and technical leadership

The most expensive mistakes are made early, in decisions no one revisits. We reduce that risk: architecture and due diligence up front, intervention when projects stall, and senior leadership when a critical build needs it.

• Solution architecture
• Technical due diligence
• Rescue of stalled projects
• Fractional engineering leadership
• AI strategy: where it pays, and where it does not