How we work

Evidence before every commitment.

Three steps, six stages. At the end of each stage you see the evidence and decide whether to spend more money, add more people, or give the system more autonomy.

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The six stages

Step	Stage	Question answered	What must be true to move on
Scope	1 Select	Which work matters most, and is it ready?	Named owner, measurable outcome, usable data
Scope	2 Diagnose	How does the work really happen, and where is the bottleneck?	Baseline agreed with the business owner
Build	3 Design	What is the lowest level of automation that solves it?	Design approved by the owner
Build	4 Prove	Does it meet the pass mark set in advance, and beat the non-AI option?	Pass mark met, zero unacceptable errors
Run	5 Pilot	Does it work for real users under enforceable controls?	Used independently, no unresolved critical incidents
Run	6 Decide	Did the value arrive, and what happens next?	Scale, iterate, or stop, with reasons

A project can stop at any stage. Stopping early with evidence is a good outcome. It saves the investment that would have followed.

What makes it work

Rules before AI

Every step is tested against four questions before any AI is considered. Most stop at the first or second. Across our four published builds, 59% of steps run on rules.

Good enough, defined first

You agree the pass mark before testing starts. It performs, or it stops.

Four owners, one off switch

Business, AI operations, engineering, and risk each own a part. Any of them can pause it.

Key terms

AI operations consulting: Redesigning document-heavy work so rules, AI, and people each do the part they are best at, then proving it works before anyone scales it.
Pass mark: The standard a system must meet before it goes live, agreed with you before testing starts. It performs, or it stops.
Unacceptable error: An error you name in advance as one the system must never make. One is enough to stop it.
Baseline: How the work performs today, measured and agreed with the business owner, so results can be compared against it.
Rules before AI: Every step is tested against four questions before AI is considered. Most stop at the first or second.
Scope, Build, Run: The three engagements: a two-week fixed-fee sprint, a fixed-fee build, and a flat monthly run.

What you see along the way

When	What lands on your desk
End of week 2	Ranked shortlist, map of the work, agreed baseline
Design	Signed-off design, with each step marked rules, AI-assisted, or human
Prove	Test results against your pass mark
Pilot	Your team running it, with training and monitoring in place
Decide	Results against the baseline and a decision memo

Diagnose toolkit

Understand the work before changing it.

Five tools take you from how people say the work happens to how it actually happens, and then to a step-by-step call on rules, AI, or a person.

Interviews. The intended process, and who is involved
Observation. The real process, workarounds included
Data inventory. Where each input lives and whether it can be trusted
Step map. Each step defined precisely enough to automate
Classification. Rules, AI-assisted, or human, step by step

Evidence before every commitment.

Rules before AI

Good enough, defined first

Four owners, one off switch

Understand the work before changing it.

Get the full toolkit

See how this would run on your workflow.