AI transformation services, measured on your own data

LLM cost optimisation

Agent spend broken down by team and task, then lowered through defaults, budgets and routing your platform team can run.

Coding agent adoption

Agents taken from a few pilot teams to the rest of engineering, with training and one shared setup.

Harness engineering

The setup that tells an agent how to build, test and change your code, kept in version control.

Evaluations on your repositories

Tools, models or settings tested side by side on changes you have already merged, before you commit to one.

Claude Code consulting and rollout

Claude Code fitted to your codebase, permissions and review process, starting with one pilot team.

Claude Code training

Workshops where your engineers practise Claude Code on their own repositories and backlog tasks.

OpenAI Codex consulting

Repositories, sandbox rules and access prepared for Codex, with training on the CLI, IDE extension and cloud agent.

Cursor consulting and team training

One agreed set of project rules, tool access and review practice for everyone who uses Cursor.

GitHub Copilot implementation

Copilot and its cloud agent configured to follow the policies and review steps you already run.

AI agents and process automation

Agents that take on the routine steps of complex cases and pass decisions to people, with a cost per handled case.

Custom model training

A small open-weight model trained for one task and compared with your current model on cost and accuracy.

AI engineering and architecture

Assistants, voice interfaces and agents in the products you sell, with the serving and retrieval underneath.

AI automation

Business processes automated step by step with rules, models and agents, tested on past cases and reported as cost per case.

AI chatbots

Chatbots and assistants for customer service and internal support that answer from your data, act through approved tools and hand over to people.

AI consulting

AI strategy, use case assessment and readiness checks from engineers who also build and measure the first system.

AI engineers for your team

Experienced AI engineers who join your team part-time or for longer and build AI features, agents and platforms with you.

Enterprise AI

Assistants, agents and private AI built into your systems, with your permissions, audit logs and a platform team that can run them.

MCP server development

MCP servers that connect approved internal APIs to AI assistants, with narrow permissions, approval for changes and tests.

RAG development

Assistants that answer from your internal documents, with permissions, citations and tests on your own questions.

AI pilot success criteria

What an AI pilot has to show before the next stage is funded, and how to write acceptance criteria before anyone chooses a tool.

Coding agent pilot design

How to compare coding agents on the same work, with random task assignment, a replay of merged changes and a decision rule agreed in advance.

Outcome versus trace evaluation

Why an agent run can finish without errors and still handle the case wrongly, and how to check business outcomes separately from traces.

RAG document permissions

How access rules travel from source systems into a retrieval index, where they get lost, and which negative tests show that a user cannot retrieve what they may not open.

Fine-tuned model evaluation

How to show that a fine-tuned model should replace the current one: acceptance criteria written in advance, a fair held-out comparison, and cost and latency in the same table.

MCP tool design

Why an MCP server should usually offer a few tools shaped around business actions instead of mirroring every API endpoint, with a worked example and a tool contract.