The billing data already held the answers. The blocker was never the data — it was that getting an answer out of it meant knowing how to query it.
A chatbot over the billing data, so a question can be asked in plain language by the person who actually has it.
The measure is simple. Does the person with the question get the answer themselves, or does it still route through whoever can run the report?
90%
Questions self-served without an analyst
Estimate; measured figure to follow.
2
Minutes to an answer, asked in plain language
Estimate; measured figure to follow.
4
Weekly report requests no longer routed through a bottleneck
Estimate; measured figure to follow.
Billing data held the answers to most operational questions, but retrieving one meant knowing how to query it — so questions queued behind whoever could run the report, or went unasked.
Read moreThe billing data held the answers to most of the operational questions anyone wanted to ask. That was never in doubt.
Getting an answer out of it was the problem, because that meant knowing how to query it. So a question took one of two routes. It went to whoever could run the report and waited its turn — or, more often and more expensively, it went unasked. A question you have to file a request for is a question most people quietly drop.
A chatbot interface sits over the billing data. The people with the questions ask them directly, in plain language, and get the answer back.
There is no query language to learn and no request to file. The skill barrier that stood between a question and its answer is simply gone.
Billing answers no longer route through a bottleneck. The person with the question gets it themselves.
The quieter win is the class of question that used to go unasked. Those never showed up in anyone's queue, so nobody counted them — and they are the ones this was really built for.
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.