Built from AI project frustration.
We help mid-sized businesses move beyond fragmented complex data and systems, and turn the right opportunities into measurable improvements using AI in business workflows.
Along the way, we build the business intelligence, knowledge and foundations for the next workflow, product or decision your business can reuse.
You've invested in AI. Is it changing your business?
Find out where the value of your AI project is
You have pilots, but nobody can show what changed.
A promising demo can't get into the real workflow.
Every new use case needs the same data, integrations and governance rebuilt.
Leadership keeps asking what the AI investment has actually returned on investment.
Every AI project should make the next one smarter. Here's the proof.
We find where AI can make the biggest difference, build what is needed around it, and make sure each project leaves the business better prepared for the next AI project.
Procurement workflows relied on disconnected systems and sensitive commercial data that could not leave the country.
We delivered a sovereign AI procurement capability inside the client's existing software and infrastructure. No client data left the country. Took a per annum of 11 projects to 57 project, without the need to remove workforce.
Sales workforce customer negotiation planning relied on static customer data and assumptions how AI could improve contract renewals.
Sales data was modelled to show how tasks could shift between sales workforce and AI Agents, giving leadership a clearer view of future roles, skills gaps and shifting sales teams from 25% ARR tens of thousands of pounds to 75% ARR million pound customer contracts.
CXO financial planning relied on static forecasts and historical assumptions that could not account for regional uncertainty or test the impact of alternative financial decisions.
An AI Financial Agent built in the organisations AI Factory, using sovereign financial data to model hospital revenue and patient LoS scenarios. This gave CXOs a clearer view of potential financial outcomes and enabled leadership to make decisions with financial foresight rather than analyst hindsight.
Every AI Project should provide a reusable business asset
Three moves help us choose the right problem, put the human-AI capability into real use, and make sure the business keeps what it learns.
Sense
We find where AI can genuinely improve the business, then test the workflow, data, economics and operating conditions before committing to a build.
Shape
We connect the workflow, knowledge, data, systems and controls needed to put the capability into everyday use.
Sharpen
We measure what changed, capture the useful knowledge and working components, and make them available to the next opportunity.
Every AI project should leave something behind.
Most AI projects solve one problem and leave the next team to start again.
We deliberately help businesses keep what is useful, connections, knowledge, decisions, controls and working patterns, so the next project can build on what the business has already learned.
Over time, individual AI projects begin to form a reusable intelligence layer across the business.
Start where your getting stuck
Whether you need an honest assessment, help building the human-AI capability, or want your own team to learn the method, we start with the same question: "What business result should this AI actually produce?"
Build your own AI capability
Learn how to choose worthwhile AI work, map the workflow, build it safely and measure whether it is actually working.
- Four weeks of hands-on AI coaching, 2 hours a week.
- A 30-minute mentor session focused on your own AI challenge.
- Run a real AI assessment yourself, using the same method we use with clients.
- Leave with a practical way to assess, build and govern AI in your own organisation.
Find out why value is getting stuck.
We review the project, workflow, data, economics and operating constraints, identify what is stopping progress, and give you a clear decision on what to build, reshape or stop.
- An honest assessment of where your AI programme is working, and where value is getting stuck.
- A clear view of the data, workflow and foundations needed to move into production
- Governance and jurisdiction designed around the realities of operating in countries.
- Clear identity and accountability for every human and AI agent acting in the system.
- A practical recommendation on what to build, reshape or stop.
Build the reusable capability.
We put one worthwhile AI capability into real use, with the data, integrations, controls and ownership needed to operate it, and leave behind the components worth reusing.
- Agreed business baseline and acceptance criteria.
- Working capability in the real workflow.
- Governance and ownership.
- Monitoring and evaluation.
- Reusable components documented for the next use case.
Frontier models. Sovereign discipline.
We use the model that fits the business problem, security requirement and jurisdiction. The model can change; the workflow, organisational memory, governance and decision infrastructure should remain reusable.
Anthropic Claude
Used where strong reasoning, tool use and agentic workflows fit the problem.
OpenAI Codex
Used for AI-assisted software engineering, automation and production development where appropriate.
SpaceX Grok
Real-time, context-aware reasoning, used where fast, current-aware generation fits the business problem.
Open source & on-prem
When data can't leave the country, we build sovereign, open-source models on in-country infrastructure, encrypted end to end.
The Intelligence Method Layer™
The tools inside the method change depending on the problem. The discipline does not.
