Billing Guidelines AI Agent

The problem

Legal billing is deceptively complex. A single misclassified time entry can mean lost revenue, a client dispute, or a compliance headache. Firms handling hundreds of billable hours across multiple matters face two costly failure modes:

• Missed billables — attorneys mark work as non-billable when it should be billed, leaving money on the table.
• Incorrect classifications — billable work gets flagged wrongly, leading to disputes and reputational damage.

The traditional fix is manual review by billing specialists. It's a bottleneck that doesn't scale, and it still misses edge cases.

The approach

I designed and built an AI agent that evaluates every invoice line item against the firm's own billing guidelines, exposed as a REST API that plugs into existing practice management systems.

Rules in plain English, not code. Billing guidelines live in a database as natural-language rules that the AI interprets directly. Firms can maintain separate rule sets per client or practice area and update them without touching code.

Context-aware decisions. A description alone doesn't tell you whether an entry is compliant. The agent enriches each entry with timekeeper data — role, practice group, seniority — pulled from an external API, so it can enforce nuanced policies like "partners shouldn't bill for administrative tasks."

Built for real invoice volumes. Large invoices are split into batches and processed in parallel, balancing API cost against response time. Results are cached on invoice content hashes, so repeated checks during iterative review return instantly.

The outcome

Every line item is now screened automatically before an invoice goes out — manual review time dropped by 70%, and the agent catches roughly 3× more guideline violations than the manual process it replaced, with structured per-item feedback instead of a marked-up PDF. The firm updates its guidelines in plain English as policies evolve, with no engineering involvement.