Artificial Intelligence
AI that works.
We decide where AI is worth building, then build it to production standard: governed, monitored and handed over as something your teams can run.
Models, platforms and tools we build with
- Claude
- Gemini
- Mistral
- Meta
- Perplexity
- DeepSeek
- Qwen
- Kimi
- Hugging Face
- Ollama
- Replicate
- Suno
- ElevenLabs
- Synthesia
- Codex
- Snowflake
- Databricks
- Bigdata.com
- Cursor
- Replit
- GitHub
- GitHub Copilot
- GitLab
- Vercel
- Netlify
- Docker
- Kubernetes
- Postman
- Linear
- Notion
- Figma
- Jira
- n8n
- Zapier
- Gamma
Our approach
AI that runs inside the business, not beside it.
Our approach pairs engineering that holds up in production with the delivery discipline to get it there: governed builds, short feedback loops and the people who will run the system involved from the start.
The result is AI that your teams trust, your risk function can explain, and your organisation can operate after we leave.
How we deliver AI that works
Our people
Delivery leadership and change management that carries the organisation with it
AI governance, risk and responsible-use controls built in, not bolted on
Engineering depth across agents, data and integration
Experience inside banks, asset managers, market-data providers and telecoms
Our delivery stack
Model-agnostic: the right model for each job, commercial or open-weight
Agentic workflows and automation wired into real processes
Integration with core systems and data, inside your controls
Evaluation, monitoring and model operations once it is live
01 — AI Consulting
Know where AI pays, before you spend on it.
Strategy, use-case selection and governance, so AI investment goes to the problems where it will earn its keep.
Most organisations do not lack AI ideas. They lack a clear view of which ones are worth building, what it will take to run them safely, and who owns the result. We work with leadership teams to sort the shortlist from the long list, size the value honestly and set the guardrails before the first model is trained.
The output is a set of decisions: which use cases to pursue, in what order, with what data, under what controls, and how success will be measured.
What we do
- AI strategy and opportunity assessment
- Use-case prioritisation and business cases
- AI governance, risk and responsible-use frameworks
- Data readiness and platform choices
- Vendor and model selection
02 — AI Engineering
Agents and automation, built for production.
Custom agents, models and automation wired into core operations: governed, monitored and tested under real load.
Most AI programmes stall in pilot, and the model is rarely the reason. Getting anything into production inside a regulated enterprise is its own discipline: integration, security, controls, monitoring and the people who will run it.
We design and build agentic systems end to end, from the first prototype to the system your teams rely on every day, and hand over something your organisation can operate.
What we do
- Agentic AI and workflow automation
- Retrieval, search and knowledge assistants
- Integration with core systems and data
- Evaluation, monitoring and model operations
- Pilot-to-production rescue
Our guiding principles
People and AI, working together
We design systems around the people who use them. AI takes the volume and the routine; people keep the judgement and the accountability.
Start with the problem, not the model
Every engagement starts with the business outcome and how it will be measured. The choice of model and tooling follows from that, never the other way round.
Built for production from day one
Security, controls, monitoring and ownership are designed in from the first prototype, so a successful pilot has somewhere to go.
Governed and explainable
In regulated environments, it has to be clear where an output came from, who owns the decision and how the system is controlled. We make that clear by design.
Build with you, not for you
We work alongside your teams so the capability stays when we leave: the code, the operating model and the know-how to run and improve it.
Short feedback loops
Working software in days, in front of the people who will use it. Evidence from real use beats opinion, and it tells us early what to change.
Change that holds
A launch is not an outcome. We plan for adoption, training and the operating model, and we measure whether the system is used and trusted.
The right team at the right time
Small, senior teams that combine business, delivery and engineering skills, with specialists brought in when the work calls for them.
Keep learning
Models and tools change month to month. We test what is new continuously and bring in what works, so you are not locked into last year’s choices.
The other half of AI²
Agile Intelligence
Programmes delivered, and rescued, in regulated environments.
Thirty years leading complex programmes in demanding, regulated environments, and turning around the ones that have gone off track, with a blend of methods matched to each one.
Get in touch
Tell us where you are. We will tell you what is possible.
Most engagements start with a conversation about the problem, not the service line.
Start a conversation