AI Strategy, Delivery & Governance
No black box. You own what we build and the rules it runs by.
- ExposureClick → Your people are already pasting company data into tools you don't control.
- EvidenceClick → You can show what the AI model decided. You can't show why, or who agreed to it.
- Already liveClick → A team built an AI tool months ago, and nobody outside it knows what it touches.
- LimitsClick → An AI system now acts on its own, and nobody has written down what it can't do.
- The dataClick → Every proposal assumes your data is ready. None of them has looked.
- The askClick → You've been asked for an AI strategy, and what exists is a pilot and a slide.
- The numberClick → The vendor's proposal came with a return on it. Nobody can show you the math.
- ProofClick → The demo was convincing, but it ran on the vendor's data, not yours.
- Build or buyClick → You're being asked to fund something your existing software might include next year.
- After the pilotClick → The AI pilot worked. Then it became nobody's budget and nobody's job.
- HeadcountClick → Any answer you give on AI and jobs becomes a promise you're held to later.
- ApprovalClick → Legal killed the last AI proposal and never said what would have gotten it through.
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Before the budget is set
We tell you what your data supports before the budget is set, and what it won't. When it will not, the cause is usually people or process rather than the data itself. Most engagements start here.
The data is a symptom. We fix the processes underneath it and get people aligned on one way of working — which holds once they see what it gives them. What comes out is consistent, accurate data.
Opportunities are identified against where the business is going, short and long term — not against what the technology happens to do. Your candidates are costed side by side and ranked by what they return. The ones that make the best demo don't necessarily pay the best. If none are ready, you hear that rather than a roadmap that defers the answer.
Cost, return, the period, and the conditions it depends on. Defensible in a room you do not control, and checkable a year later against what happened. If the return depends on fewer people, you hear that before you commit, not in year two.
Through build and go-live
The software development lifecycle (SDLC) doesn't change because you're building AI — requirements, testing, release, support, all of it. These are the parts that do.
Built into the systems you already run, alongside the people who own them. The work is identity, permissions, and where two systems have to agree. Expect less new software than the proposal implied. Where what you already own will do the job, we say so, even when that ends the conversation.
Software that acts, not advises. What it settles alone, what it routes to a person, what it does when uncertain — written down, and yours to set. We tune it against real work until it holds.
An agreed standard for good enough, measured against real cases before release. Afterward, uptime still matters, but it isn't enough. Output quality has to be monitored too, because AI behavior can move without anyone touching the code.
We stay past go-live, through the weeks when people use it in ways nobody scripted. Handover is when your team runs it without us. Your people are trained, not told.
Across all of it
One named person accountable, with a plan, a budget and a reporting line to the board or the sponsor — including when your team, an integrator or a vendor is doing the building. We resell nothing, and we will say when a timeline is not real.
Whatever you answer to — the data rules, the security standards, the ones written into your customers' contracts — AI gets no exemption and we ask for none.
Policy and acceptable use, model and vendor risk, and where a person stays in the loop. Where your data goes, who can see it, and whether any of it trains a model outside your control. Written for the people who operate it as well as the ones who audit it.
AI, from whether your data can support the idea, through what gets built, to what the system is allowed to decide on its own and who answers for it when someone asks. Take any part of it, or the whole program. Working across all of it is what lets us tell you a vendor's number is wrong before you spend it, rather than after. Start there →
Their first AI project. And what it nearly cost them.
- 10×the build
- SOC 2the audit
Hired to lead it. We stopped it twice — once for the data, once for the architecture the vendor wanted. Read what happened →
on qualifying projects, a portion of the engagement effort may be capitalizable rather than expensed. Worth confirming with your finance team.
Three other ways in
Design · build · both
Not sure we're a fit? Not sure where to start?
Both are good reasons to call.
Start a conversation →- AI readiness assessment
- Data readiness & remediation
- Use-case triage & prioritization
- Business case & ROI modeling
- Integration into existing systems
- Agent design & autonomy boundaries
- Human-in-the-loop & escalation design
- Evaluation & output quality monitoring
- Model & vendor risk assessment
- AI policy & acceptable use
- Data residency & training-use controls
- Deployment, training & hypercare
- Program leadership & board reporting
- Obligations, evidence & audit readiness
- AI strategy & operating model
- Shadow AI discovery & inventory
- Build vs buy analysis
- Pilot design & proof of value
- Pilot-to-production transition
- Model explainability & decision logging
- AI governance & approval workflow
- Workforce impact & role redesign
- Agentic AI design & deployment
- Multi-agent orchestration
- Agent tooling & function calling