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Seyhun Akyurek
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Multi-Agent Systems

Gavel

Evidence-driven claims adjudication — analyzes disputes, images, and order data to determine validity, fraud risk, and severity.

Azure AI Foundry
Azure Computer Vision
Python
FastAPI
Azure Database for PostgreSQL
Azure Cache for Redis
React
75% faster
Resolution Time
98%
Fraud Detection
96%
Accuracy
4.6/5
Customer Satisfaction

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Gavel · Agent Mesh

ORCHESTRATORMulti-Agent SystemsEvidenceACTIVEFraudNODE 02AdjudicationNODE 03CommunicationNODE 04AnalyticsNODE 0512345678
Step 1Instant

Claim Intake

Claim submitted through portal, email, or API with supporting evidence.

Form submissionEvidence uploadData extractionInitial triage

Technical Design

Evidence-driven claims adjudication engine with multi-modal analysis. AI agents analyze disputes, images, and order data to determine validity and fraud risk.

System Components

Evidence Analyzer

Multi-modal evidence assessment

Fraud Detector

Pattern-based fraud identification

Adjudication Engine

AI-powered claim decision making

Communication Service

Automated claimant updates

Analytics Dashboard

Claims trend and fraud pattern analysis

Data Flow

Claim Intake
Evidence Analysis
Fraud Screening
Policy Mapping
Severity Assessment
Adjudication
Communication
Analytics

Technology Choice

Azure AI Foundry + LangChain

Azure AI Foundry provides multi-modal capabilities for image and text analysis. LangChain orchestrates complex adjudication workflows with policy matching.

Alternatives Considered

Custom ML Pipeline + Rules Engine, AWS Rekognition + LlamaIndex

Core Frameworks

Azure AI Foundry

Multi-modal evidence analysis

LangChain

Adjudication workflow orchestration

Azure Computer Vision

Image and video analysis

FastAPI

Claims processing API

Implementation Plan

Evidence Pipeline

4 weeks
  • Multi-modal ingestion
  • Evidence scoring
  • Authenticity detection

Fraud Detection

4 weeks
  • Pattern analysis
  • Behavioral signals
  • Risk scoring

Adjudication Logic

4 weeks
  • Decision engine
  • Policy matching
  • Explanation generation

Communication & Analytics

2 weeks
  • Status updates
  • Trend dashboard
  • Fraud pattern reporting

Key Milestones

Evidence analysis <5 minutes
Fraud detection >98%
Adjudication accuracy >96%
Resolution time 75% faster

Risk Mitigation

Human review for complex claims
Appeal process for disputed decisions
Continuous model improvement from outcomes

Key Features

Evidence Analysis

AI analyzes submitted evidence including images, messages, and order data.

Fraud Detection

Pattern-based fraud detection using historical claims and behavioral signals.

Severity Scoring

Automated severity assessment to prioritize high-impact claims.

Policy Matching

Map claims to applicable policies and coverage rules automatically.

Decision Support

Provide recommended decisions with explanations and supporting evidence.

Appeals Management

Handle appeals with additional evidence review and escalation workflows.

How It Works

1

Claim Intake

Instant

Claim submitted through portal, email, or API with supporting evidence.

Form submission
Evidence upload
Data extraction
Initial triage
2

Evidence Analysis

< 5 minutes

AI analyzes all submitted evidence for authenticity and relevance.

Image analysis
Document verification
Communication review
Evidence scoring
3

Fraud Screening

< 2 minutes

Automated fraud detection using pattern and behavioral analysis.

Pattern matching
Behavioral analysis
Network screening
Risk scoring
4

Policy Mapping

< 1 minute

Map claim to applicable policies and coverage rules.

Policy retrieval
Coverage analysis
Rule application
Benefit calculation
5

Severity Assessment

< 1 minute

Calculate claim severity and prioritize processing.

Impact assessment
Urgency scoring
Priority assignment
Queue routing
6

Adjudication

< 3 minutes

AI makes or recommends claim decision with full explanation.

Decision generation
Explanation creation
Evidence citation
Consistency check
7

Communication

Instant

Notify claimant of decision with detailed explanation.

Decision delivery
Explanation sharing
Next steps
Appeal information
8

Analytics & Learning

Ongoing

Continuous learning from outcomes to improve future decisions.

Outcome tracking
Model updating
Pattern learning
Process improvement

Multi-Agent Architecture

Evidence Agent

Analyzes submitted evidence including images, documents, and communications.

  • Image analysis
  • Document parsing
  • Communication review
  • Evidence scoring
  • Authenticity check

Fraud Agent

Detects fraudulent claims using pattern analysis and behavioral signals.

  • Pattern detection
  • Behavioral analysis
  • Network analysis
  • Risk scoring
  • Investigation support

Adjudication Agent

Makes claim decisions based on evidence, policy, and risk assessment.

  • Decision making
  • Policy application
  • Evidence weighing
  • Explanation generation
  • Consistency checking

Communication Agent

Manages claimant communications and status updates.

  • Status updates
  • Information requests
  • Decision delivery
  • Sentiment monitoring
  • Escalation triggers

Analytics Agent

Provides insights on claim trends, fraud patterns, and operational metrics.

  • Trend analysis
  • Pattern identification
  • Performance metrics
  • Predictive analytics
  • Reporting

Use Cases

E-Commerce Disputes

Resolve buyer-seller disputes with automated evidence analysis.

Insurance Claims

Process insurance claims with AI-powered assessment and adjudication.

Payment Disputes

Handle payment disputes and chargebacks with automated investigation.

Product Returns

Automate return eligibility decisions based on policy and evidence.

Service Complaints

Resolve service quality complaints with evidence-based decisions.

Fraud Investigations

Support fraud investigations with pattern analysis and evidence review.

Interested in Gavel?

Get in touch to discuss how this solution can be tailored to your needs.