The same work keeps coming back for another round because "ready" and "done" were never written down. We define both — and AI reviews each brief for completeness before work starts, while a person sets the bar.
Common pain points
The work keeps coming back for another round because what "done" means was never written down.
Work starts from an incomplete brief, so the first version is missing things nobody named up front.
Review happens at the end, when a gap means redoing the work instead of catching it before it started.
Acceptance is a matter of opinion, so two reviewers send the same work back for different reasons.
The same loop repeats across projects because nobody captures why the work bounced last time.
Everyone blames effort or skill, when the real cause is that "ready" and "done" were never agreed.
A rework reduction system writes the definition of ready — what a brief must carry before work starts — and the definition of done — the acceptance criteria — for the deliverable types that loop. AI reviews each incoming brief for completeness against the definition of ready, flags what is likely to bounce, summarises the recurring loop causes, and prepares the checklist. A person sets the bar and accepts what is done — AI does not approve done.
How we work
We pick the deliverable types that actually loop and write the definition of ready and the definition of done with the people who do and review the work.
We connect AI to review each brief for completeness before work starts, flag likely-rework, and summarise the loop causes so the definitions get sharper.
We set who accepts done and hand over a runbook the team operates and edits.
What we build
Area 1
We write what a brief or request must contain before anyone starts — the inputs, the constraints, the examples — for each deliverable type that loops. It is agreed with the people who do the work, so it reflects what they actually need, not a wish list.
Deliverable
A definition of ready per deliverable type: the inputs and constraints a brief must carry before work starts.
Area 2
We write what "done" means as acceptance criteria a reviewer can check, so acceptance stops being a matter of opinion and two reviewers stop sending work back for different reasons.
Deliverable
A definition of done: the acceptance criteria for each deliverable type, written so a reviewer can check them.
Area 3
We turn the definition of ready into a checklist at intake, so an incomplete brief is caught and completed before work begins — not after the first version comes back. The check is the moment that breaks the loop.
Deliverable
An intake checklist: the definition of ready as a gate a brief passes before work starts.
Area 4
We connect AI to review each brief against the definition of ready, flag what is likely to bounce (missing inputs, ambiguous acceptance), summarise recurring loop causes, and prepare the checklist. What AI may never do — approve that work is done — is written down. AI reviews and prepares; a person sets the bar and accepts.
Deliverable
An AI completeness-review map with the limits written: what AI reviews/flags/summarises, and what only a person accepts.
Area 5
Each time work bounces, we capture why in a short log — a missing input, an unclear criterion — and feed it back into the definition of ready and done. The definitions get sharper instead of the same loop repeating across projects.
Deliverable
A loop-cause log: why work bounced, feeding the definitions of ready and done so the loop stops repeating.
Area 6
We hand the system over as a runbook: the definitions of ready and done, the intake checklist, the AI review limits, and the loop-cause log. The goal is for it to live without us — the team can add a deliverable type and tighten a definition without rebuilding it.
Deliverable
A runbook with the definitions of ready and done, the intake checklist, and the AI limits the team owns.
Work starts from a complete brief, so the first version stops missing things nobody named up front.
"Done" is written as acceptance criteria, so reviewers stop sending the same work back for different reasons.
Completeness is checked before work starts, so a gap is caught up front instead of after a full round.
AI reviews each brief and flags likely-rework — and a person sets the bar, with the limit that AI never approves done.
Why work bounced is captured and fed back, so the same loop stops repeating across projects.
The definitions and the checklist live in a runbook the team operates and edits, not in one reviewer's judgement.
A workflow audit diagnoses across all your flows and tells you what to fix where. This reduces one rework loop you already feel — it does not survey everything. If you do not yet know where the rework is, the audit comes first; if you know exactly which deliverable keeps bouncing, this is the work.
No. Approvals are about who signs off once work is submitted. This is about making the work right before it is submitted — the definition of ready and done and the completeness check at intake. They are upstream and downstream of each other, not the same work.
No. We do not sell or build a project-management or task-tracking tool. The deliverables are the definitions of ready and done, the intake checklist, the loop-cause log, and the AI review limits — set up in the tools your team already uses. We name current tools as examples; we do not sell or integrate any of them.
No. QA and testing check software after it is built. This is upstream of any deliverable — a brief, a report, a design, a campaign — making sure it starts ready and is accepted against written criteria. It applies wherever work loops, not only to code.
No. AI reviews each brief for completeness before work starts, flags what is likely to bounce, and summarises the loop causes — but a person sets the bar and accepts what is done. The rule that AI never approves done is written into the system.
It does not include a full workflow audit (that is a separate page), an approval workflow, a project-management tool, or QA/testing of software. It also does not promise zero revisions — it promises that the ones that remain are about judgement, not missing inputs. If what you need is one of those, we will tell you and point you to the right work.
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Read moreBook a 30-minute call to look at where work bounces back today. We will talk through which deliverables loop, what "ready" and "done" should mean for them, and where an AI completeness check before work starts can catch the gaps — and what the team can realistically run. If the real issue is broader than one loop, we will tell you and point you to a workflow audit instead.
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