AI-Powered Billing Dispute Resolution Digital Worker
This digital worker deploys an 11-agent AI system that conducts comprehensive billing investigations in approximately 8 minutes with 98.2% accuracy.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Billing Dispute Resolution interface showing 11 AI agents with ~8min resolution time, 98.2% accuracy metrics, dispute details form, and AI agent team panel with specialized roles.
Agentic Workflow Orchestration view displaying agent pipeline status, 30-step tool invocation flow, active Anomaly Detector agent with context window usage and latency breakdown.
Investigation Results dashboard showing workflow metrics with 55s duration, 32 LLM calls, $0.17 cost, 11 phases completed, and detailed execution steps for data retrieval and validation.
Bill Verified Accurate outcome with 96% confidence showing weather impact explanation, key agent decisions from all validators, and expected impact metrics including tenant satisfaction.
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Billing dispute investigations require coordinating multiple specialized analyses in proper sequence while managing inter-agent communication, synthesizing findings, and making final resolution recommendations.
Core Logic
Coordinates the 11-phase investigation workflow using Claude 3 Opus with temperature 0.3. Manages task delegation to specialized agents, monitors workflow progress through pending/running/completed states, synthesizes findings with weighted confidence aggregation, determines if human review is required based on confidence thresholds, and generates final recommendations with consensus validation across all agents.
Data Retrieval Agent
Billing investigations require comprehensive data from multiple sources including meter readings, billing history, property records, and weather data. Manual data gathering is slow and may miss critical context.
Core Logic
Uses GPT-4 Turbo with temperature 0.1 for deterministic data retrieval. Queries MDM systems for meter readings (heat, water, electricity), retrieves 12-month billing history, fetches property specifications, and obtains weather data for consumption normalization. Validates data completeness and quality (100% data quality target), documenting all data sources for audit trails.
Smart Grid Analyzer Agent
Billing disputes may stem from meter malfunctions or IoT connectivity issues. Without verifying smart meter health and data integrity, investigations may proceed with unreliable source data.
Core Logic
Queries real-time smart meter status via LoRaWAN gateway, analyzing signal strength (92%), battery levels (87%), firmware versions, and tamper detection status. Verifies meter health scores (98%), checks calibration validity, analyzes grid load conditions, and confirms data quality (100%). Reports renewable energy contribution (42.5%) and grid frequency stability for comprehensive infrastructure verification.
Consumption Analyzer Agent
Tenants often dispute bills that show consumption increases without understanding weather impacts, seasonal patterns, or occupancy factors. Raw consumption comparisons can be misleading without proper normalization.
Core Logic
Analyzes consumption patterns using GPT-4 Turbo with statistical methods. Compares current period to 12-month historical baseline, applies weather normalization using heating/cooling degree days, calculates variance percentages (e.g., raw +57.9% → normalized +12.3%), and performs z-score analysis (threshold 2.5) to identify statistical outliers. Distinguishes between weather-driven increases and genuine anomalies.
Billing Validator Agent
Billing calculations involve complex rate structures, cost allocation methodologies, and rounding rules. Even small errors can significantly impact tenant bills and erode trust.
Core Logic
Recalculates all billing components from source data using Claude 3 Sonnet with temperature 0.1 for precision. Verifies rate applications against current tariff schedules, checks cost allocation methodology compliance, validates rounding rules, and compares recalculated total to original bill. Reports exact match status with component-level verification.
Regulatory Compliance Agent
Billing must comply with EU EED (Energy Efficiency Directive), German HeizKVO (Heating Cost Ordinance), and GDPR data privacy requirements. Non-compliant billing can result in regulatory penalties and legal challenges.
Core Logic
Checks compliance using Claude 3 Sonnet against EU EED Articles 9-11 (individual metering, billing information, cost allocation), HeizKVO requirements (30-70 allocation split, billing deadlines), and GDPR standards (PII redaction, encryption, consent management). Verifies AES-256-GCM encryption, documents compliance status, and generates audit-ready compliance reports.
Anomaly Detector (Peer Comparison) Agent
Tenants want to know if their consumption is reasonable compared to similar units. Without peer benchmarking, it's difficult to contextualize individual consumption patterns.
Core Logic
Compares tenant consumption to peer units using same-floor, same-size criteria. Calculates percentile rankings, determines deviation from building average, and computes savings versus average. Uses statistical deviation analysis to confirm tenant is within normal distribution.
Carbon Footprint Agent
Tenants and organizations increasingly want to understand the environmental impact of energy consumption. Carbon footprint calculations require emission factors, energy source data, and sustainability benchmarking.
Core Logic
Calculates CO2 emissions using GHG Protocol methodology with German-specific emission factors. Breaks down emissions by source (heating 74.8%, electricity 22.3%, water 2.9%), assigns sustainability scores (72/100) and ESG ratings (B), and generates carbon offset recommendations. Identifies reduction opportunities (e.g., 615 kg CO2e/year potential through green electricity tariff, smart thermostat, optimized heating).
Demand Response Agent
Tenants may not realize opportunities to reduce costs through peak shifting and demand response participation. Without load analysis, potential savings remain unrealized.
Core Logic
Analyzes peak consumption patterns using historical data, identifies shiftable loads, queries tariff schedules for time-of-use rate differentials, and calculates potential savings from demand response participation. Reports enrollment status, historical earnings, and potential annual savings through automated load shifting.
Energy Forecast Agent
Tenants want to know what to expect on future bills. Without predictive modeling, they cannot plan for seasonal variations or identify savings opportunities.
Core Logic
Generates consumption and cost forecasts using LSTM Ensemble + XGBoost models with high historical accuracy. Forecasts monthly costs, analyzes weather impact on future consumption, and identifies savings opportunities. Provides confidence levels and explains expected bill trajectory based on seasonal normalization.
Response Generator Agent
Tenant communications require professional, empathetic, and clear explanations with supporting evidence. Manual response drafting is inconsistent and time-consuming.
Core Logic
Generates personalized tenant responses using Claude 3 Opus with temperature 0.7 for natural language. Retrieves relevant templates from vector store (Pinecone) using RAG, optimizes for Grade 7-8 readability, analyzes sentiment (0.82 positive), and structures responses with sections (greeting, summary, evidence, explanation, action, closing). Creates comparison visualizations (bar charts, line charts, pie charts) to illustrate findings clearly.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization