What Does AI Document Review Look Like Inside a Personal Injury Firm?

At Wolfson & Leon, a Miami personal injury firm, every record uploaded to the case file is read with OCR and an AI model in about 15 to 30 seconds. The platform classifies the document, pulls out the providers and bills, and shows what is still missing from the file. Staff confirm the deadlines the AI extracts, and any new rule the AI learns takes effect only after staff approve it.

This field note is drawn from the Wolfson & Leon case study.

Why does a personal injury firm need AI document review?

Because a personal injury case is a stack of documents with deadlines attached. Each case moves through intake, treatment, demands, and sometimes litigation, and every stage depends on medical records, bills, letters, and court orders, plus the handoffs between staff who need them. Reading each one, deciding what it is, and noting what it changes is slow work that repeats on every case, and it is exactly the kind of reading AI now does well.

What happens when a record is uploaded?

Four things, in about 15 to 30 seconds:

1. OCR reads the document, so scanned records and faxes become text.
2. The AI classifies it: a medical record, a bill, a court order, and so on.
3. It extracts what matters: the providers and the bills, attached to the case.
4. It updates what is missing. The case file shows which records are still outstanding.

By July 2026, the firm had loaded 1,483 case documents into the platform, and AI had analyzed 620 of them. In August 2026, every department's workflow was tested end to end in production.

How does the platform handle deadlines?

It finds them; people confirm them. A litigation cockpit calculates service and discovery deadlines and pulls dates from court orders for staff to confirm. A compliance center flags statute-of-limitations dates and open liens. In Florida, most negligence claims now have a two-year limit, which is why the same date check matters from the first intake call onward.

How does the AI learn the firm's rules?

Slowly, on purpose. The platform learns rules from how staff actually work, but a rule takes effect only after repeated evidence and staff approval, and client names are scrubbed out before anything is learned. That is the approval lane from which tasks AI can fully automate in practice: the AI proposes, a person decides, and only then does the rule run on its own.

What else does the platform run?

The documents feed the rest of the case:

• Gated stages: Intake, Opening, Case Management, Pre-Litigation, Litigation, and Closing, with handoffs that cannot be skipped, role-based access, and a full audit log
• Fees and reductions: fees computed from the retainer the client actually signed, with a workflow for medical bill reductions
• Intake: Lily, an AI intake agent whose answers come from more than 40 answers dictated by the firm's attorney

The same idea, records turned into something a team can act on, is the product behind RadiusDocs, which builds medical chronologies whose answers cite the source page.

What would another firm need to build this?

Clear stages, written rules, and people who own the decisions. The AI handles the reading at volume; the firm decides what each document means for the case. That split is how Cream Digital builds AI workflows for firms and practices.