We scope, build, and deliver AI systems, with a timeline that doesn’t move.
Don’t start with AI tools. Start with business outcomes. We help you adopt, implement, and get value from AI, from readiness to ROI.
Is AI earning its keep in your business?
AI works when it’s built into the way work gets done, not added on the side. That takes two things at once: foundations that can keep improving, and ways of working designed for people and AI together.
We build both and stay accountable for the results. We start where you are and move you forward, from strategy to running AI day to day.
What we do.
For any organisation still asking whether AI can do this. Not a demo. Not a science project.
- See a working Claude build run on your own data.
- Measure it against a KPI that you choose.
- End with a clear go, pivot, or stop decision.
- Leave with the code and a costed route to production.
- Discovery: the use case, the sponsor, the KPI
- Sprint one: stand up the build, wire in your data
- Sprint two: iterate on real data, run evaluations
- Readout: the numbers and a costed route to production
For engineering organisations where adoption is patchy, security blocks rollout, or nobody can measure the gain.
- Roll out Claude Code securely, with the right access and controls.
- Get developers working to shared conventions across your teams.
- Add custom commands and skills shaped to your stack.
- Track adoption on a dashboard that shows the real return.
- Technical setup: SSO, provisioning, allowlists, telemetry
- Phased launch: a pilot group first, then champions per team
- Training: sessions, project conventions, custom commands, CI
- Scaling: rollout across teams, with office hours and support
- Recover the business logic buried in the code, documented at last.
- Move onto a modern stack, tests built as you go.
- Prove the new matches the old, run side by side.
- Cut over in phases, reusing what works across the estate.
- Assess: extract the logic, map dependencies, prove one subsystem
- Migrate: structure-preserving translation, with generated tests
- Validate: run old and new in parallel, compare outputs
- Scale: reuse proven patterns across the estate
For any organisation that wants AI but isn't sure where to begin.
- Get a clear score across the dimensions that matter.
- See your gaps ranked by the impact of fixing them.
- Compare yourself against peers who have done this already.
- Leave with a prioritised roadmap your leadership can act on.
- Scope: objectives, documents, interview plan
- Discovery: stakeholder interviews, infrastructure and data review
- Scoring: each dimension benchmarked, gaps ranked by impact
- Readout: an executive summary and a prioritised action plan
For organisations whose people are using AI ad hoc, or whose training so far changed nothing.
- Get every team trained on their real work, role by role.
- Take people from curious to fluent on the tools you pay for.
- Grow in-house champions who keep the momentum going.
- Leave with reusable role guides and adoption you can measure.
- Scope: pick the departments, roles, and use cases
- Install: Claude Enterprise set up and configured, where you need it
- Train: hands-on sessions per team, on their own work
- Champions: train the people who will carry it forward
- Measure: usage reviewed after each wave, gaps retrained
For organisations that rolled out AI and saw little uptake, or whose people feel wary.
- Get a clear adoption plan built around your actual rollout.
- Train people on their real tasks, role by role.
- Build a champion network and the comms to carry it.
- Measure adoption and keep it climbing after the launch fades.
- Sponsors: secure active, visible executive backing
- Segment: map impacted groups, skill gaps, resistance points
- Enable: role-based training on real tasks, champions per team
- Reinforce: office hours, adoption measurement, continuous refresh
For organisations that know staff use AI but cannot see it.
- Get one ranked list of the tools in use, no guesswork.
- Know the risk each carries, by data and business unit.
- Put a usable AI policy and incident playbook in place.
- Give staff a safe, sanctioned option they will prefer.
- Discovery: network, software, and expense signals build the inventory
- Amnesty survey: a no-punishment self-report catches the rest
- Risk scoring: every tool mapped to business unit and data class
- Governance: acceptable-use policy, incident playbook, safe default
For organisations running AI in production with nobody to operate it.
- Have it monitored under an agreed service level.
- Control spend and keep guardrails on, without a new hire.
- Get a real response when something breaks, day or night.
- Raise quality over time, even on systems you did not build.
- Onboard: map models, agents, and pipelines, set service levels
- Instrument: tracing, evaluation suites, cost attribution, guardrails
- Operate: real-time alerts, incident management, human review where needed
- Improve: continuous evaluation, model routing, monthly reviews
Methods that deliver.
The constraint is execution.
Organisations understand the potential. Production is where plans meet infrastructure, governance, and real processes. We build for production from day one: every system ships with evaluations, monitoring, and cost controls. And the job continues after go-live: we watch adoption, measure quality, control spend, and expand what works.
See how the Diagnostic worksRunning 50 or more seats, or working under sector rules?
We scope, build, and deliver AI systems for mid-market companies, and we choose the right models for the job, whether that is Claude, another model, or a mix.
If you need a global AI operating model across dozens of business units, use a large firm. If you run 50 or more seats and need AI working this quarter, that is us. Either way, we will tell you honestly whether we are the right size for it.
Book a 30-minute callFrequently asked questions
Every engagement is a fixed fee scoped to headcount and site count, agreed before we start. No hourly billing and no open-ended discovery phase. We do not publish figures because the honest range is wide. The number comes out of the Deployment Diagnostic, inside a business case with a spend baseline and the payback to expect.
A short, fixed-scope build that proves one AI use case against a KPI you choose. You get a working Claude solution, measured results, the code, and a clear go, pivot, or stop decision. If the numbers do not justify production, we say so.
Stalled AI programmes usually fail for three reasons: the use case was chosen by enthusiasm rather than impact, nobody owned the outcome after the pilot, or the data wasn't ready and that only surfaced late. The sprint deliberately front-loads all three.
Every build hands over into operation: evaluations, monitoring, cost control, guardrails, and incident response under an agreed service level, with monthly reviews. We do not ship and disappear.
Most organisations don't. We begin by identifying high-impact business opportunities, assessing your AI readiness, and creating a practical implementation roadmap.
No. Our approach is designed to fit into the systems your teams already use. We improve existing workflows instead of forcing employees to adopt completely new ways of working.
No. Assessing your data landscape is part of the work, not a prerequisite for it.
Low adoption after training is the most common failure point across the industry, and it's why we track usage data and include follow-up clinics if adoption is below target.
Before implementation begins, we define clear business objectives and success metrics. After deployment, we track productivity improvements, adoption, operational efficiency, and other KPIs.
Yes. Managed AI Services starts with an onboarding audit of your existing models, agents, and pipelines, whoever built them. We document the gaps, stand up monitoring and service levels, and operate from there.
No. The survey runs on an amnesty basis: a no-punishment self-report designed to surface what network signals miss. The goal is a safe, sanctioned default people actually prefer.
Yes, and you are under no obligation to use us for it.
Start where it costs you nothing.
Thirty minutes. No pitch. We'll walk through your current AI setup, name the three things we'd fix first, and tell you honestly whether you need us at all. Or take the free Fit Check: ten minutes, a scored verdict and one top opportunity, sent to your inbox.
No obligation. A senior consultant, not a sales rep.