AI Strategy

Confidential, vendor-neutral AI guidance from practitioners who build production systems, not just advise on them.

AI Readiness Workshops

The problem

Your board is being asked to approve significant AI investments. Your CTO has opinions. Your vendors have pitches. What you don't have is an independent, vendor-neutral perspective from someone who has actually built and shipped production AI systems, not just evaluated them from a distance.

The AI landscape moves fast. New models arrive monthly. Frameworks are still maturing. The difference between what works in a demo and what works in production is enormous, and most advisory firms can't tell you which is which because they haven't done the work themselves.

What we do

We run confidential 1-2 day workshops with your leadership team and relevant stakeholders. These are not sales pitches for a platform. They are objective, authoritative and interactive sessions designed to give you a clear-eyed understanding of what AI can and cannot do for your specific business.

We start with the current state of the art, covering frontier models, open-weight alternatives, agentic frameworks, and emerging protocols like MCP and A2A. We then focus entirely on your business: your data, your customers, your competitive landscape, your constraints.

Our perspective comes from hands-on experience across both Silicon Valley and the UK/European ecosystem. We've worked with leading AI platform providers, built sovereign AI applications using locally hosted open-weight models, and run AI strategy workshops for Fortune 500 C-suite leaders and Non-Executive Directors in financial services.

Workshop coverage

Part 1: The landscape
  • Generative and agentic AI: what's real, what's hype
  • Architecture options and trade-offs
  • Frontier models vs. open-weight alternatives
  • Data sovereignty and the case for sovereign AI
  • The engineering-to-design transition: where we are and what it means
  • Regulatory considerations: EU AI Act, FCA, Consumer Duty
  • Hands-on demonstrations with real systems
  • Honest assessment of what goes wrong
Part 2: Your business
  • Your specific business goals and pain points
  • Where AI can create genuine value vs. where it's theatre
  • Build vs. buy: the nuanced version
  • Data readiness and what needs fixing first
  • A prioritised, executable roadmap
  • Getting started without betting the company

What you get

By the end of the workshop, your leadership team will have a shared, informed understanding of your AI opportunity, grounded in reality rather than vendor promises. You will have a practical roadmap that accounts for your actual data quality, technical estate, and organisational readiness. And you will have asked every question you wanted to ask, in confidence, of someone who will give you a straight answer.

Production AI Advisory

The problem

You've run a proof of concept. It worked. The demo impressed the stakeholders. Now you need to put it into production, and you're discovering that the gap between "it works" and "it works reliably, at scale, with real data, under governance" is far larger than anyone expected.

This is not unusual. The investment industry's own data shows that 96% of firms believe AI can revolutionise their operations, but only 41% are scaling it as core business. The gap is not ambition or budget. It's the hard engineering and governance work that sits between a demo and a production system.

What we do

We work alongside your technical team to solve the problems that don't exist in the demo but will break you in production:

  • Integration with real systems, legacy APIs, and the edge cases that clean demo data never surfaces
  • Governance including audit trails, approval workflows, and access controls that regulators and compliance teams require
  • Trust and safety to prevent misuse, harmful outputs, prompt injection, and jailbreaks
  • Observability so you know what the system is doing, why it made a particular decision, and where it's struggling
  • Guardrails to keep AI behaviour within acceptable boundaries, particularly for regulated industries
  • Drift monitoring to catch when model performance degrades over time, which it will

We've built production AI systems using open-weight LLMs, agentic pipelines with iterative reasoning loops, and event-driven architectures. We've dealt with hallucination in financial data, infinite loops in agentic reasoning, and the specific challenges of making probabilistic software reliable enough for regulated environments.

We also help you navigate the shift from deterministic to probabilistic software. Your IT organisation is built for determinism: same input, same output, test once, trust forever. AI doesn't work that way. We help you build the testing, monitoring, and operational frameworks that this new reality demands.

What you get

A production AI system that actually works, with the governance, observability, and operational maturity that your business requires. Not a demo that impresses in a meeting room and fails in the field.

Getting started

Contact us with a brief description of your situation, and we'll get in touch.

Get in Touch