AI agents
Task-specific agents that use tools, retrieve context, follow constraints and hand off to a person when automation should stop. Actions are scoped, logged and reversible where possible.
Service
AI agents and features built to run in production, not just in a demo.
When a use case is clear and your team wants a partner to build it, we design and ship it with the same evidence-first approach we use in our own products. Every system comes with its tests, its monitoring and a defined way to fail safely.
How it fits together
Who it is for
- Product teams adding an AI feature to an existing application
- Operations teams automating a multi-step workflow
- Companies with large document collections that need grounded, cited answers
- Teams that built a prototype and need it made reliable
Problems we see
- A prototype that works on the demo inputs and nothing else
- Answers that sound right but cannot be traced to a source
- Agents that take actions without limits, logging or a way to stop
- No plan for ownership, cost control or maintenance after launch
What we do
What is included.
Retrieval and knowledge systems
RAG pipelines with source citations, access control and an honest 'not found' path, for technical, regulated and internal content.
AI features in your product
Search, summarisation, extraction, classification and drafting features integrated into your existing stack and design.
Compliance and security workflows
Evidence collection, documentation generation and structured workflows for regulated processes. This is the same work that sits behind CRA Toolkit.
Prototype hardening
Take an existing prototype and add evaluation, error handling, observability, cost controls and deployment.
How it runs
The process, step by step.
- 01
Define
Agree the user, the task, the constraints and the measurable definition of done.
- 02
Evaluate first
Build the test set before the feature, so progress is measured from the first commit.
- 03
Build
Implement in short iterations, reviewed against the evaluation results.
- 04
Harden
Add guardrails, monitoring, cost limits and failure handling.
- 05
Hand over
Documentation, runbooks and training so your team can own it.
Deliverables
What you walk away with.
- Working system deployed in your environment
- Evaluation suite and baseline results
- Monitoring and cost dashboards
- Architecture and operations documentation
- Source code and infrastructure definitions that you own
Questions
AI Engineering: common questions
What is a production AI agent?
A production AI agent is a task-specific system that uses a language model together with tools and data to complete a defined job, with limits on what it may do, logging of what it did, and a handoff to a person when it is unsure. It is designed and tested for real workloads rather than demonstrations.
What is RAG and when should a system use it?
Retrieval-augmented generation (RAG) retrieves relevant documents before the model answers, so the answer is grounded in your source material instead of the model's memory. It suits regulated, technical and knowledge-heavy content where answers must be traceable and unsupported claims are unacceptable.
Do we own the code?
Yes. Source code, prompts, evaluation sets and infrastructure definitions are delivered to you. We agree this explicitly in the scope of work.
Which models and cloud do you build on?
We choose the model and platform that fit your quality, cost, residency and security requirements, and design the system so the model can be swapped. The evaluation suite is what makes swapping safe.
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