Ship AI that
survives contact
with reality.
Demos are easy. Landing an AI capability that people trust, auditors accept and finance can justify is a delivery problem. This is the operating system for that work — a staged playbook, a readiness gate with teeth, a live RAID register and the artefacts you would otherwise rebuild from scratch on every programme.
Weighted across scope, quality, adoption, cost and assurance signals for every programme in flight.
2,677 active users served
Six tools that replace the spreadsheet archaeology
Every AI programme rediscovers the same lessons at the same cost. These are the instruments that stop that happening on yours.
Staged delivery playbook
Frame → Prove → Pilot → Harden → Scale → Sustain, each with exit criteria, artefacts and the trap that kills programmes at that stage.
Open playbook →
Launch readiness scorer
Twenty-four weighted checks across value, data, evaluation, responsible AI, engineering and adoption. Produces a defensible go/no-go, not a feeling.
Run the assessment →
RAID & exposure register
Risks, assumptions, issues and dependencies with a probability × impact heat map tuned for AI-specific failure modes.
Manage RAID →
Portfolio health dashboard
Five-signal health per programme, cost per successful task, adoption trend and the single blocking note that matters.
View portfolio →
Responsible AI gate
Twenty-two controls grouped by transparency, data, quality, safety, oversight and operations — each with the evidence a reviewer will ask for.
Work the gate →
Artefact toolkit
Pilot charters, eval plans, gate-review packs, impact assessments, rollout plans and incident runbooks. Copy, fill, ship.
Grab artefacts →
From ambition to retirement, honestly
Frame
Turn an ambition into a falsifiable problem statement.
Exit: A single sentence stating what changes, for whom, by how much, by when.
Prove
Establish technical feasibility against a real evaluation set.
Exit: Measured quality clears the threshold the business owner signed up to.
Pilot
Put it in front of real users under real constraints, with containment.
Exit: Users choose to keep using it when nobody is watching.
Harden
Make it boring: observable, reversible and supportable.
Exit: You can change the model on a Friday without fear.
Scale
Grow adoption deliberately, protecting quality and cost.
Exit: Adoption curve and quality curve both hold as volume rises.
Sustain
Run it as a product, and retire it honestly when value ends.
Exit: Value is evidenced, or the capability is retired without drama.
Most AI programmes fail at the gate, not the model.
Run the readiness assessment with your leads in the room. It takes eleven minutes and usually ends an argument that has been running for a month.
Start the assessment →