Case studies
Real work, with the clients left unnamed.
What the job was, what was built, what changed, and what still has limits. Numbers come from the work itself. Where there is no measurement, I say so.
Client names and identifying details (city, team size and anything specific enough to place them) are left out on purpose. Clients are not asked to be named, so the blur is deliberate.
Case study
A small firm: client drafts back in hours, every send still reviewed
A firm of lawyers and assistants where drafts waited in a queue. Now a lawyer can have a draft reply ready to review within hours, and nothing goes out without a person pressing send.
Case study
A dental practice: finding why payments landed with the wrong provider
A long-standing problem in the practice's reports, traced to its cause and confirmed with the software company. AI helped with the thinking. No patient records were sent to it.
Case study
A field-sales company: more than 70,000 messy records to an 84-name call list
Two systems that did not talk to each other, full of duplicates and phone numbers typed into notes. The goal was simple: find the people who needed the service and never booked.
Case study
A real estate agent: one instruction instead of four systems
An agent on a larger team was entering every new lead in four places. Now one line does it, and on her first day on her own she cleared work that had been waiting.
My own practice
My own books: a year of bookkeeping from bank statements
Before I set this up for anyone else, I did it for my own business. AI sorts the transactions. An accountant still reviews and files the return.
Start with a conversation
Tell Devan about the repeat work that takes the most time in your practice. If AI is a good fit, he will say so. If it is not, he will say that too.
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