A workflow can't be fixed, or automated, until the human context inside it has been unpacked.
The people running the workflow don't have the time or the mandate to see it whole. The people who can see it all don't have the end to end context to build the fix.
We supply what's missing.
In every large operation, a small number of cross department workflows absorb a disproportionate share of cost. The rules were never made explicit. The cost sits hidden across teams, and the problem never gets fixed.
A planning decision shifts the cost onto logistics. A logistics workaround becomes a commercial exception. Commercial patches it by hand, and finance closes the books before the fix ever traces back to where it started. No single team owns it. No single budget carries it. That's not a failure of management. That's what happens when a workflow was never made legible enough to measure.
It happens in the DC and on the floor. It happens in planning and the back office.
Underneath almost every one of these workflows sits the same root cause: data that's incomplete, inconsistent, or contradicted across the systems meant to share it. Decision logic, ownership, exception rules, data contracts: documented, validated, and proven on real volume. Everything we produce is yours. We leave capability behind, not dependency.
AI is an accelerant here, not a starting point: it strengthens what's already clear and exposes what's still broken.
COO, VP Operations, and the equivalent in procurement, compliance, finance and commercial: the leaders who carry this cost in their P&L.
A working, validated workflow. Full decision logic. RACI and SLAs. Data contracts. Unit economics before and after.
The industry has a name for this: tribal knowledge. It lives in two or three people who've been there long enough to know the exceptions, the workarounds, and the judgment calls nobody wrote down, because everyone who mattered already knew them. Software only works with the data it's given. Getting the rest out takes a person, in the building, tracing it end to end with the people doing the work. That's what Stage 1 actually is.
That tacit knowledge is only half the picture. The other half is organizational context: the map of who owns each handoff, where a cost crosses a team boundary that no single budget carries, and why the workaround exists in the first place. No tool holds that map, and no single person inside the business usually holds all of it either, it's split across three or four teams who each only see their own piece. Stage 1 traces both together: what people know, and how the workflow actually moves between them.
The risk nobody has on a register: when those two or three people leave, that knowledge doesn't degrade. It's just gone.
You already know which workflow costs you the most. Somebody on your team flagged it months ago. What's missing isn't the idea, it's the hands: the hands to sit still long enough, across every team it touches, to nail down its real cost and impact, and the hands to bring the technical, AI enabled point solution that actually clears it. The whirlwind of daily operations eats both. Everyone stays busy fixing symptoms because nobody has the two things it takes to fix the constraint itself.
That's what we supply: thirty years of enterprise transformation pattern matching, fifteen years of it inside Microsoft across APAC, Europe, Japan, and Seattle, and the last two years embedded directly in early enterprise AI deployments across retail, supply chain, banking, energy and ESG, to name the cost fast. Behind that sits a delivery team across Seattle, Auckland, Madrid and the UK, so the fix gets built rather than specified.
One conversation, with the people who actually run the workflow, not just leadership. We map where the cost sits and tell you if the numbers justify going further. No preparation. No commitment.
Your own team already knows how to fix pieces of this. What almost nobody has is the uninterrupted time to trace the whole workflow end to end, across every team it touches. Stage 1 buys that time: every person, every process, every spreadsheet. You get the workflow named, the cost documented in real unit economics, and the annualised saving quantified as a specific number.
The second thing most teams don't have the hands for: actually building the fix. We bring the working point solution, including AI where it belongs, not just a spec for your engineers to get to eventually. The percentage is agreed against the exact figure Stage 1 delivered, so the fee comes out of recovered money rather than your budget.
Before any technology decision, the real question is simple: is this a process problem or a capability problem? Fix what the process itself can fix first, that's faster, cheaper, and lower risk. Bring in new technology only once a genuine constraint is confirmed, one the process itself can't overcome. Stage 1 is how that question gets answered with a real number behind it, not a guess.
You don't have to take our word for it:
Sources: McKinsey, "The State of AI in 2025," published November 5, 2025 (1,993 respondents, 105 nations). S&P Global Market Intelligence, 2025 Voice of the Enterprise Survey. Madrona Venture Group, "Harnessing Enterprise Value, the ROI of AI," August 2026 (150 enterprise decision makers).
Big 4 diagnose and propose. The diagnosis ends with a programme sized to their rates. We prove before we propose, and you own every output from day one.
We pick whatever actually solves the specific problem: a script, a piece of automation, any vendor's tool if that's genuinely the right fit, a resourcing change, more hands or fewer. Nothing here commits you to a license or a stack. You don't inherit a platform because we happened to use one.
An FDE's job is to get their employer's platform installed and running in your operation, not to fix what's underneath it. FDEs are lab specific by design too: once a team is trained on one lab's patterns, retraining on a competitor's stack is friction no manager volunteers for, and an embedded engineer sees your proprietary workflows and data flow back into that vendor's own model tuning. You need us first, not instead: we fix the actual constraint, agnostic to whatever platform comes next, so if an FDE does show up later, they're landing on a workflow that already works, not one they'll faithfully automate.
No license, no seats, no maintenance contract, nothing to renew. The fee is a share of money you are already recovering, for a fixed term, and then it ends.
Two years embedded in early enterprise AI deployments across retail, supply chain, banking, energy and ESG, in Southeast Asia, the US and the UK. Fifteen years at Microsoft. Twenty years founding and advising businesses in New Zealand. Earlier work includes assessing and commercialising a PE backed, rules based workflow platform across SE Asia.
Business analysis in Madrid. Data model and strategy at CIO level in Auckland. Senior development in New Zealand. Two developers and customer success in Seattle. Enterprise build through specialist partners. The people who diagnose the workflow are the ones who fix it.