AI Consultancy London: Strategy + Implementation for UK Mid-Market
An AI consultancy that ships the production system. Strategy, RAG pipelines, agent workflows, and LLM evaluation — built into your business by a senior team that has run real operating environments through real change curves.
London-based. Founder-led by Jamie Buchanan. Mid-market focus. In-person where it matters; remote the rest of the time.
Operator-grade pedigree
Jamie Buchanan leads Rogue's AI consultancy practice. Seven years as CTO at Vanarama, scaling the business from a £20m operation to a £200m AutoTrader acquisition — with full ownership of the engineering, product, data, and ML stack across an FCA-regulated vehicle leasing marketplace.
The Vanarama tech-org through that curve was not a research lab. It was an operating business that needed credit-risk models, pricing engines, dealer-network analytics, and decisioning systems that worked in production every minute of every day. The data foundations, governance posture, and ML deployment patterns underneath were built by the same person now leading this practice.
That experience is the spine of Rogue's AI work today — RAG pipelines for ecommerce and content-heavy businesses, LLM observability and evaluation harnesses, structured-output agent systems for operational workflows, and AI-readiness audits with implementation pathways for UK mid-market buyers. Read Jamie's full bio.
The AI consultancy that ships
Most AI consultancies stop at strategy. The deliverable is a deck, a use-case matrix, and a recommendation that someone else builds. The engagement ends before any of it touches production — which is also where most of the genuine constraint lives.
The interesting decisions in an AI build are not the ones in the strategy phase. They are decisions about chunking strategy on a real corpus, about how an evaluation harness scores a model that produces structured output, about what happens when a frontier-model vendor changes a token price overnight, about the human-in-the-loop boundary in a regulated workflow, and about the cost-control envelope when a single prompt accidentally costs £2,000 in a day. None of that lives in a strategy deck. All of it lives in the production system.
Rogue is built to deliver both halves. Strategy on Monday, build against it on Tuesday, hand off a maintainable system at the end. The pedigree underneath is operating-business engineering at scale — not research, not advisory-only, not LLM-wrapper agency work. The practice is opinionated, founder-led, and small enough that the people doing the work are the people in the strategy room.
See our deep-dive on LLM evaluation for non-ML teams for a worked example of the practice in writing.
What we deliver
Five outcomes our London engagements consistently produce. Each one is a real artefact your team owns and can maintain — not a recommendation, not a slide.
From AI roadmap to production agents in 90 days
A roadmap is not the deliverable — the working system is. We compress the gap between strategic decision and production feature so the business stops waiting for AI to land and starts compounding the capability.
LLM eval pipelines that prevent quality drift
An LLM feature in production is a probabilistic system the rest of your stack treats as deterministic. Eval pipelines catch quality drift before customers do, with measurable accuracy targets agreed up front rather than a vibes-based "looks good".
Production RAG that retrieves the right thing
Most first-attempt RAG builds fail not on the model but on retrieval. Chunking strategy that reflects the content, embedding choices that survive a corpus refresh, and evaluation across the retrieval layer specifically — not just the final answer.
AI-readiness audits with implementation pathways
A structured audit of data foundations, governance posture, integration surface, and security model — with a prioritised pathway to production. The audit is the start of the engagement, not the end of it.
Structured-output agent systems for operational workflows
Multi-step agents that call tools, query systems, and hand off to humans — built with proper guardrails, cost caps, observability, and a clear regulatory envelope. The kind of system that survives a Monday morning rather than impressing a Friday demo.
Engagement model
Three engagement tiers, structured around the actual constraint. The right tier depends on whether the business needs senior AI thinking at the exec table, an implementation team that ships production features, or an embedded capability that runs an AI workstream alongside the in-house team.
Advisory & Strategy
From £15,000 / month
AI roadmap, readiness audits, vendor and architecture review, and exec-level translation of the AI category into operating reality. The right tier when the business needs senior AI thinking before the build phase begins.
Build & Deliver
From £35,000 / month
Implementation sprints — production RAG, LLM features, agent workflows, evaluation pipelines, and observability. Ships against an agreed roadmap, in your codebase, with your team alongside.
Full Partnership
From £50,000 / month
Embedded AI capability — Rogue runs an AI workstream alongside the in-house team, with shared roadmap, shared on-call, and a defined plan for transferring capability over the engagement.
Day-rate work for scoped pieces — typically a focused AI-readiness audit, an architecture review, or a discovery sprint — is £1,500 per day. Final commercial shape is agreed in the second conversation, not on the website.
Why London
AI engagements are unusually dependent on in-person collaboration during the discovery, integration, and handover phases. The constraints in an AI build live in operating reality — the messy joins between systems, the informal workflows that nobody documents, the regulatory edge cases nobody volunteers in a brief — and most of that surfaces in conversation rather than in tickets. London proximity means in-person time when in-person time matters.
The other reason is regulatory fluency. UK ICO guidance on AI and automated decisioning, the EU AI Act risk-classification framework, sector-specific regimes in financial services and healthcare, and the practical realities of UK GDPR all sit on top of every meaningful AI engagement. Working with a London-based consultancy that operates inside that regulatory envelope every day, rather than translating it from a US frame, removes a recurring class of avoidable risk.
Most of our ICP — London fintech, ecommerce, professional services, and venture-backed scaleups — is also London-based, which compounds the proximity advantage. We know the operating context, the talent market, the investor base, and the regulatory cadence of the businesses we serve.
Recent thinking and work
The practice publishes its working framework — not abstract AI commentary, but the actual decision frameworks, evaluation patterns, and architectural shapes we use on engagements. A small set of representative pieces:
- LLM evaluation for non-ML teams →
How to set up evaluation pipelines that prevent quality drift without needing a research-grade ML team.
- AI-readiness checklist for ecommerce →
A structured audit framework for UK mid-market ecommerce buyers — data, governance, integration, and use-case prioritisation.
- RAG vs fine-tuning: a decision framework →
When retrieval beats fine-tuning, when fine-tuning beats retrieval, and the production-cost calculus underneath the choice.
- Composable commerce, where AI plugs in →
Composable architecture is the substrate most production AI ecommerce features sit on. The shape, the trade-offs, and the integration patterns.
For broader pillar context see AI & data services for the UK-wide capability overview, and platform engineering for the underlying systems work most production AI features depend on.
Frequently asked questions
What does an AI consultancy actually deliver? +
How is this different from a strategy-only consultancy or a Big Four practice? +
Do you only work with London companies? +
How do you handle UK and EU regulatory compliance? +
Who actually does the work? +
What does an engagement cost? +
How long does a typical engagement run? +
How do we get started? +
Ready for the AI conversation that ships?
Tell us where the business is, where AI fits the operating plan, and what you have already tried. We will tell you honestly whether AI is the constraint worth solving — and if it is, how we would build it. 30 minutes. London-based or remote.
Book a 30-minute intro call