Case Study
Specialty law firm lifts billable-hour margins with agentic AI case intake
Results
Scale
Cut case-evaluation manual effort materially
Speed
Beat statute-of-limitations deadlines on aging cases
Risk
Built audit-grade trail across case decisions
Company Profile
Specialty law firm widely recognized as a leader in healthcare reimbursement recovery, resolving claim denials and contract disputes with commercial and government payors for hospitals and providers. Approximately 200 employees, with a referral-built reputation as the healthcare industry’s law firm of choice.
Challenge
Case intake drowned in medical-record review
Case intake was painful at scale. Our client, a specialty law firm built on referral business in healthcare reimbursement recovery, evaluated every potential case by reading medical records that ran hundreds, sometimes thousands, of pages. A backlog of thousands of claims meant intake staff were buried in document review before they could even decide whether to take a case.
The cost wasn’t just throughput. Every claim that aged past its statute of limitations before the firm could evaluate it was revenue lost forever, and every case that didn’t qualify but still consumed evaluation time was margin given away.
Solution
Agentic AI case intake and evaluation
Our client engaged WonderBotz to leverage proprietary agentic-AI frameworks and rapidly assemble an end-to-end case intake automation. The agent pulled medical records from Epic, parsed both structured tabular data and unstructured clinical notes, summarized each file, and then applied rule-and-precedent logic to flag cases eligible to move forward with litigation.
With the agent disqualifying unsuitable cases and surfacing qualified ones, attorneys redirected their billable hours from manual review to active litigation. The firm began reaching cases sitting in their backlog ahead of statute deadlines, retaining matters that would otherwise have aged out unactionable.
The agent’s audit trail also tightened the firm’s compliance posture and gave operations a clean foundation to extend agentic automation into adjacent workflows.
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