AI-Powered Legal Bill Review Digital Worker

Core Logic

The Orchestrator Agent manages the complete workflow lifecycle by coordinating agent execution order, maintaining shared context between agents, handling retry logic with exponential backoff, tracking execution state (idle, thinking, planning, executing, analyzing, generating, self_correcting, awaiting_approval, completed, error), and ensuring proper completion of all downstream agents before finalizing results. It implements trace ID correlation for end-to-end observability and manages token consumption tracking across all agent calls.

Core Logic

The Compliance Agent applies rule-based detection for block billing (identifying multiple semicolons in narratives indicating bundled tasks), validates minimum narrative length requirements (10+ words), cross-references task descriptions against prohibited task databases, and performs ABA Model Rule compliance verification. It integrates with client billing guideline databases to apply client-specific rules and generates compliance scores (0-100) with detailed violation reports including severity classification and remediation recommendations.

Core Logic

The Narrative Agent analyzes narrative specificity using NLP techniques including word count analysis, action verb detection, quantifier identification, and proper noun extraction. It calculates a specificity score (0-1 scale) for each entry and generates AI-enhanced narrative suggestions for entries falling below threshold. Enhanced narratives maintain factual accuracy while adding relevant detail such as specific document names, case references, and quantified work product. The agent preserves original narratives while providing enhanced alternatives for attorney review.

Core Logic

The Rate Validation Agent enforces client rate caps per timekeeper level (Partner, Associate, Paralegal) by cross-referencing billed rates against configured cap tables. It performs statistical anomaly detection using Z-score analysis to identify unusual rate variances that may indicate data entry errors or unauthorized rate changes. The agent handles complex scenarios including blended rates, alternative fee arrangements, and rate progression schedules, flagging discrepancies for review while auto-approving compliant entries.

Core Logic

The Predictive Risk Agent implements a logistic regression model achieving 0.96 AUC-ROC accuracy for write-off probability prediction. Feature inputs include: compliance issue flags, narrative vagueness scores, rate validation failures, excessive hours indicators, and timekeeper seniority level. The model outputs probability scores (0-1) with explainable feature contributions, enabling prioritized review of high-risk entries. Predictions include quantified financial impact calculations to support business case for intervention.

Core Logic

The Anomaly Detection Agent implements an Isolation Forest algorithm for unsupervised multi-dimensional anomaly detection. It analyzes entry features including: billed rate, hours worked, total amount, narrative quality score, and timekeeper patterns. The algorithm identifies approximately 15% of entries as anomalies requiring review, with each flagged entry including anomaly scores, contributing factors, and comparison to baseline patterns. Self-correction capabilities allow the agent to refine its detection by cross-validating against historical false positive patterns.