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Built a diagnosis engine that reads a whole GoHighLevel account every week

It opens one account read-only, rebuilds what customers actually experienced that week, reviews every workflow and every AI agent, then ranks the causes and prepares the fix for a person to approve.

7: phases of review
9: specialists, each on a different part of the account
5: stages a finding passes before it stands
Every: surface of GoHighLevel, read whole

The problem

Weekly reporting on a live account gave the team a conversion rate and never a reason. Leads stopped replying, stopped booking and stopped showing up, and the numbers recorded all three without explaining any of them, so every week opened with the same argument about what to fix first.

Account scope and collection. It opens one account, fixes the exact completed week in that account's own timezone, and records whether each area came back complete, partial, empty or unreadable.

Conversation collection and protection. Under fifty conversations in a week every one is read. Above that it samples, but a complaint, an opt-out, a failed delivery or a missed call is never the thing that gets dropped.

Customer journey and measurement. Records that sit apart in the account joined into one journey: the lead arriving, the messages, the booking, whether they showed up, the sale.

Five-stage specialist review. Every workflow and every AI agent reviewed on its own, then three senior reviewers read the whole account independently and none of them is told what to look for.

Root-cause investigation and prioritisation. Every finding has to say where it happens, why, how certain that is and what would prove it wrong, before it is ranked against the others.

Action pack and implementation. The engine stops before the account. A person approves or rejects each proposed change, and only approved work gets built.

Weekly memory and self-correction. This week's causes compared against earlier weeks, so a problem that is no longer visible is marked absent rather than assumed fixed.

Trust, safety and honest limitations. Read-only, one account per run, bounded collection, and raw evidence kept separate from its encryption key.

What shipped

Every workflow and every AI agent reviewed whole, never sampled
Causes ranked on evidence and commercial impact rather than the order they were found in
Week over week, a cause that disappears is marked absent rather than assumed fixed
Read-only by construction. Every live change still waits on a person to approve it

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