AI Clarity Audit for a multi-state fleet reconditioning company
A complete AI audit for a mobile fleet-reconditioning company: workflow map, ten ROI-ranked AI opportunities, a full CRM data model, a phased roadmap, and a prompt library. Eight documents, theirs to keep.
Challenge
The client reconditions delivery vans across Texas, Washington, Oregon, and California, with a plan to scale from 6 locations toward 50. The operation ran on hustle: quotes hand-built through raw ChatGPT sessions, payment cycles stretching to ten days, and the data that should drive decisions living on the shop floor instead of in a system. Scaling that by eight would have scaled the chaos with it.
Approach
We ran the full AI Clarity Audit and delivered eight documents: a workflow map with the gaps called out where data falls on the floor, ten AI and automation opportunities ranked by ROI, a complete CRM data model designed for their operation, a phased implementation roadmap, a prompt library tuned to their actual quoting and operations language, a quick-wins sheet for week one, and an execution file their own AI tools can run so the roadmap does not depend on us. An honest audit too: several of the biggest findings were process problems, not AI problems, and the report says so.
Result
- 8 deliverable documents, theirs to keep
- 10 AI opportunities ranked by ROI
- 6 to 50 locations the roadmap is built to scale across
Details
This is the audit product doing exactly what it says. No pitch deck, no platform recommendation with a commission behind it. A map of how the business actually runs today, a ranked list of where AI and automation genuinely pay off, and the build plan to get there in phases sized for a company in the middle of scaling.
The part most audits skip: the deliverables are executable. The CRM model is specified to the table level, the roadmap is phased with dependencies, and the whole package ships with a file the client’s own AI tooling can pick up and run. The point of an audit is not a document. It is that the next dollar spent lands on the right problem.