1
Days from event to feedback
Estimate; measured figure to follow.
100%
Roles with defined KPIs
Estimate; measured figure to follow.
12
Review cycles per year, up from 1
Estimate; measured figure to follow.
Reviews were being done. What was missing sat underneath them: nobody had agreed what good actually looked like in a given role, so there was no standard to review anyone against.
Explicit KPIs, defined per role. AI tracks progress against them continuously and surfaces what it finds to the manager and the employee at the same time.
The test was never whether reviews got done on time. It was whether a manager and their employee, asked separately how things were going, would give the same answer.
Performance reviews were episodic and largely subjective. With no agreed definition of what good looked like in a role, feedback arrived late, inconsistently, and often as a surprise.
Read morePerformance reviews were episodic and largely subjective. They happened when they happened, and what they concluded depended a good deal on who was writing them.
The real problem sat underneath that. There was no agreed definition of what good looked like in a given role — no standard to measure against. Without one, feedback could only ever arrive late and inconsistently, and it often landed as a surprise to the person receiving it. A surprise in a review is the clearest sign that it should have been said months earlier.
I built the review system on explicit KPIs, defined per role. That is the part that usually gets skipped: before you can review someone continuously, somebody has to say plainly what the job is.
With the standard written down, AI tracks progress against it continuously and surfaces feedback to both the manager and the employee — rather than saving it all up for one annual conversation.
Managers and staff see the same picture of performance, against the same standard, on an ongoing basis instead of once a year.
That was the whole point of it. A review should be the summary of a conversation both people have already been having — not the first time either of them hears where they stand.
If the answer you need isn't here, ask me directly.
No, and I'd be sceptical of anyone selling you that. AI is a force multiplier, not an employee replacement. The businesses that get real value from it are the ones that understood their own operations first — the technology amplifies whatever process you already have, including a bad one.
n8n for orchestration, Claude and OpenAI models for the reasoning layer, and whatever systems the business already lives in — EHRs, practice management, CRMs, Microsoft and Google's stacks. The goal is to fit the tools to the operation, not to move the operation onto a tool.
In the work you do hundreds of times a month without thinking about it. Billing, intake, routing, follow-up, reconciliation, reporting. It rarely pays off in the interesting, judgement-heavy work people assume it will — that's where you want your humans.
No. Most of the leverage I've seen is at mid-size operators — big enough that the repetitive work is genuinely expensive, small enough to change how they work without a committee.
By understanding how the business actually runs, which is usually not how the org chart says it runs. I map where the time and the handoffs are going before proposing anything. You cannot automate a process you haven't understood, and most failed AI projects are failures of that step, not of the technology.
Yes. Reach out through the form and pick 'Speaking' — it routes straight to me.