Money is moving faster than judgement — and not in the direction it’s being sold to you.
Sources: LangChain State of Agent Engineering and IDC, 2026 · FinOps Foundation State of FinOps, 2026 · IBM Global CEO Study, 2026
What we do
The Twelve-Month AI Plan
Whether you’re starting with fresh budget and a blank page, or you’re two years in with pilots everywhere and production nowhere, the problem is the same: no ranked plan. What to back, at what cost, in what order, and what to set aside — with the reasoning written down. Newcomers skip the expensive years of finding out what doesn’t work. Teams already deep in AI get one plan instead of fifteen opinions.
The AI Cost Review
Before you build, or after the billMost enterprises spend two to three times what they need to on AI. We find where the money is going and cut it — model selection and routing, caching, context, workload placement — against your actual architecture and invoices, not benchmarks. Run early it shapes the design before the design gets expensive; run late it typically recovers 30 to 60 per cent.
Licence to Value
Before renewalCopilot seats, Gemini licences, agent platforms, committed cloud and data spend — signed, paid, and renewing. At renewal the question is what came back. We establish what is genuinely being used and by whom, where the value is real, what is blocking the rest — and what to expand, redistribute or renegotiate before the renewal date arrives. Nobody who sold you those licences has any interest in you buying fewer.
Method
How two weeks is enough.
Not because we work fast, but because of where we’ve sat. Twenty years writing and pricing the proposals enterprises buy means knowing where the cost sits, where the risk hides, and which assumptions are doing the heavy lifting.
The ranking isn’t an opinion, it’s a method: every initiative scored against the same questions of value, true cost, data readiness, integration weight and ownership. And we’re structured for two weeks — no team to mobilise, no methodology to license, no downstream work to sell. Nothing sits between you and the answer.
Why us
AI programs rarely fail on the AI.
They fail where enterprise programs have always failed: data that isn’t ready, integration nobody priced, dependencies nobody owned, and plans built to win approval rather than survive delivery.
Twenty years of doing exactly this work for some of the largest enterprises in Australia and beyond — architecting the programs, pricing them, leading them into delivery. The last few years spent doing it with AI: strategy, agentic architecture and governance for major enterprises, most recently inside one of Australia’s largest network operators. The technology is new. The reasons it fails are not.
Independence
Nobody who profits from the spend can advise on reducing it.
The cloud vendor profits when the inference bill rises. The systems integrator profits from building more. The team whose design choices created the cost is the team you’d otherwise ask to fix it.
The large consulting firms have the same problem in a better suit — their strategy work is priced to win the implementation that follows. We have nothing downstream. When the two weeks end, the engagement ends, which means the answer only has to be right, not billable.
How we work
Fixed duration, fixed scope, delivered by the person you met — not a team you’ve never met. We don’t implement, we don’t run programs, we don’t staff teams. Every engagement ends the same way: a concise written plan, the numbers and reasoning behind every call in it, and a working session taking your leadership through it before we finish.
Sectors
Telecommunications · Utilities and energy · Financial services · Large regulated enterprise