We put senior AI leadership inside your business and a working agent into production in 8–12 weeks — built into the systems your teams already use, and measured against a number you set before we start.
Built by the team that ran the Automation & AI Centre of Excellence inside Australia's largest retailer.
Almost every stalled AI programme we see is one of these — at any size of organisation. None is a technology failure. All three are sequencing failures.
A platform is bought before the problem is defined. Multi-year contract signed, implementation kicked off, and the team's actual job becomes finding use cases that fit what was already purchased.
Cost: the licence, plus a year spent solving for the technology instead of the business.
A top-tier firm produces a genuinely excellent document. Nobody implements it — the firm's product was the report, and nobody internally knows how to turn slide 94 into a running agent.
Cost: the fee, plus the credibility you spent getting it approved.
A senior AI lead arrives on a six-figure package to no data infrastructure, no executive alignment and no operational clarity. They leave inside two years and the knowledge leaves with them.
Cost: all three at once — money, time and institutional memory.
We assess every engagement against the same five. Programmes that reach production have all of them. Programmes stuck in pilot are missing three or four.
Tied to one named business outcome, measured on one number — not an ambition on a slide. If the team cannot repeat the target back to you a month later, it was never agreed.
First measurable result in weeks, inside a workflow people already use. The discipline is refusing to let “we’re still scoping” stretch past the first fortnight.
One accountable name, measured on the outcome the way a CFO is measured on cash. Ask four executives who owns your AI programme; if you get four answers, this is the force you are missing.
The agent changes in days when your process does. No re-engineering, no new contract, no waiting for a vendor release cycle.
Outcomes reviewed on a cadence by someone with the authority to course-correct. Most AI failures are not dramatic — they are a good agent nobody maintained.
All five, or the other four are good intentions.
Every figure below comes from a delivered engagement, measured against a baseline taken before the agent went live. Average return across projects: 250%.
Around five hours of manual work per application, 25 analysts buried in backlog, and no way to grow the book at scale. An agent now extracts what it needs from the submitted documents and runs the operational process end to end.
Three to five hours each, every week, extracting data and building analysis for senior managers. Two agents went live: a personalised performance briefing, and a commercial analyst available on demand. They weren't hired to search. They were hired to decide.
Manual specification reviews across hundreds of SKUs — slow, inconsistent and reactive, on a process where one missed allergen declaration carries recall-scale cost. An agent now checks specs, product information files, regulations and labels in seconds, under human sign-off.
A manual validation step inside the standard customer process, with an eleven-day average wait. An agent reads payslips and rental documentation, produces verified values and raises review flags for the operators who need to see them.
Procurement, category management, sales and revenue growth all waiting — more than a week for a standard profitability analysis, so teams often ran it themselves instead of doing their own job. The agent handles what a governed system can answer correctly every time and reroutes anything needing judgement.
“I went from one to two weeks of work to a few hours.”
“Next level game changer for us. We are very excited by what this means for us moving forward.”
Banking, retail, regulated manufacturing. Lending, commercial, compliance, data. These teams sit in different industries, answer to different executives and are measured on different things. The diagnosis was the same every time: expensive, skilled people spending most of the week on high-volume work that never required their judgement.
That is what we look for first. Not where AI could be applied, but where your best people are losing capacity — searching, waiting, or making decisions without the information they need.
Not a roadmap. A working agent in your system of record, producing a number you can take to a board.
One workflow named. One success metric defined. One executive sponsor confirmed. No sponsor, no start.
Configured against your real data and tested in shadow mode on historical cases — not a demo environment.
Running in production under human review, producing measurable output in the same week it launches.
A monthly outcome review with authority to change things, and scoping for the next agent.
Most organisations start with a sprint and continue into implementation. Some need the department function standing behind them permanently.
Find where value is actually trapped, size it, and leave with a plan someone can execute on Monday.
We build and deploy the agent, inside the workflow your team already uses, and stay until it is earning.
The leadership function of an internal AI department, without building one first.
Thirty minutes. It is the opening exercise from our Value Creation Sprint, scoped to one workflow you choose. You leave knowing what the first agent would be and roughly what it is worth — whether or not we end up working together.
