AI & Automation
AI built for one specific problem.
When an off-the-shelf AI tool isn't enough, we design and build purpose-built AI applications, such as knowledge retrieval over your own documents and assistants that understand your domain.
When custom AI makes sense
- 01
Your knowledge is the product
Answers must come from your own documents, policies or data, with sources people can check.
- 02
Your domain is specialised
General tools miss the terminology, rules and context of your field.
- 03
AI is a feature you sell
You're building AI into a product for your own customers and need it reliable at scale.
What we build
- 01
Knowledge retrieval (RAG)
Systems that search your documents and data and answer with citations, kept up to date as content changes.
- 02
Domain-specific assistants
Assistants grounded in your terminology, rules and processes, with clear limits on what they'll do.
- 03
AI applications
Complete applications around an AI capability: interface, data pipeline, permissions and admin tools.
- 04
Evaluation
Test sets drawn from real questions, so quality is measured before launch and after every change.
- 05
Guardrails
Input screening, scoped tools, rate limits and human handover when the AI isn't confident.
- 06
Operations
Monitoring for quality, cost and latency, with controls to switch models when needed.
How we work
- 01
Define success
We agree the questions the system must answer and how good is good enough.
- 02
Prepare the data
We collect, clean and structure the knowledge the system will draw on.
- 03
Build and evaluate
We build the solution and test it against real questions, improving it until it meets the bar.
- 04
Launch and monitor
We release it to users with monitoring, and keep improving it from real usage.
Custom AI questions
Retrieval-augmented generation: the system first finds the relevant passages in your documents or data, then the model answers using only that material, citing its sources. It keeps answers grounded and current.
We reduce it significantly by grounding answers in your sources, requiring citations, testing against real questions and handing over to a person when confidence is low. No AI system is perfect, so we design for that.
Usually not. Most solutions use your existing documents and records rather than training a model, so a well-organised knowledge base is the main requirement.
Milo, the assistant on this website, is one: it answers from our own content, drafts project briefs and books calls, with guardrails and human confirmation for anything it sends.
Have a problem general AI can't solve?
Tell us what the system should know and who will use it. We'll outline how we'd build it.