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Multi-Agent Systems • Active

Gavel: Trust & Safety

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

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

75% faster
Resolution Time
98%
Fraud Detection
96%
Accuracy
4.6/5
Customer Satisfaction
Free 20-min discovery + 48h ROI audit Production-grade, not a demo4 weeks to first value
✓
✓
✓
Evidence analysis <5 minutes • Human review for complex claims • Borderline flagged
Why this vs status quo
Do nothing
75% faster lost
Manual Multi-Agent Systems
Build in-house
6–12 months
Hire + maintain
Gavel
98%
Weeks, not months
Evidence Analysis • Fraud Detection
Save ~10×

What ships

  • Evidence Analysis — AI analyzes submitted evidence including images, messages, a…
  • Fraud Detection — Pattern-based fraud detection using historical claims and be…
  • Severity Scoring — Automated severity assessment to prioritize high-impact clai…
  • Policy Matching — Map claims to applicable policies and coverage rules automat…
  • Decision Support — Provide recommended decisions with explanations and supporti…
  • Appeals Management — Handle appeals with additional evidence review and escalatio…

Human signs every critical step • Audit trail included

Sample output & architecture

Gavel — Production Blueprint
Dated 18 Aug 2026 • #GAV-2026-041
Resolution Time: 75% fasterFraud Detection: 98%Accuracy: 96%Multi-Agent Systems
Agents
Evidence Agent • Fraud Agent • Adjudication Agent
Stack
Azure AI Foundry, Azure Computer Vision, Python, FastAPI
Workflow preview
1. Claim Intake2. Evidence Analysis3. Fraud Screening4. Policy Mapping
Data flow: Claim Intake → Evidence Analysis → Fraud Screening → Policy Mapping → Severity Assessment → Adjudication → Communication → Analytics
Azure AI Foundry + LangChain • Azure AI Foundry provides multi-modal capabilities for imagev1.0 • Audit trail

Why Azure AI Foundry + LangChain wins

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

Alternatives: Custom ML Pipeline + Rules Engine, AWS Rekognition + LlamaIndex

How Gavel works — Instant to first value

Claim Intake → Evidence Analysis → Fraud Screening → delivery. Full 8-step trail collapses below.

01 • Instant
Claim Intake
Claim submitted through portal, email, or API with supporting evidence.
02 • < 5 minutes
Evidence Analysis
AI analyzes all submitted evidence for authenticity and relevance.
03 • < 2 minutes
Fraud Screening
Automated fraud detection using pattern and behavioral analysis.
Full 8-step audit trail
1
Claim Intake • Instant
Claim submitted through portal, email, or API with supporting evidence.
2
Evidence Analysis • < 5 minutes
AI analyzes all submitted evidence for authenticity and relevance.
3
Fraud Screening • < 2 minutes
Automated fraud detection using pattern and behavioral analysis.
4
Policy Mapping • < 1 minute
Map claim to applicable policies and coverage rules.
5
Severity Assessment • < 1 minute
Calculate claim severity and prioritize processing.
6
Adjudication • < 3 minutes
AI makes or recommends claim decision with full explanation.
7
Communication • Instant
Notify claimant of decision with detailed explanation.
8
Analytics & Learning • Ongoing
Continuous learning from outcomes to improve future decisions.

Which Multi-Agent Systems use case is yours?

Tap your model — see the exact hinge we test.

E-Commerce Disputes

Resolve buyer-seller disputes with automated evidence analysis.

Do nothing
75% faster wasted
  • • Manual Multi-Agent Systems process
  • • No audit trail
  • • No evidence for inspector/investor
Build in-house
6–12 months • Hire team
  • • Hire + train + maintain
  • • No re-run if product changes
  • • Hard to show investor quickly
Gavel — 98%
8 steps • Agents • Evidence
  • Evidence Analysis
  • Fraud Detection
  • Daily watch + re-run

Multi-agent mesh — orchestrated

Live system presentation

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

Objections, answered

If it’s not answered, you’ll hesitate. So we answer it here.

Evidence-driven claims adjudication — analyzes disputes, images, and order data to determine validity, fraud risk, and severity. Delivered as 5 specialized agents + 8-step workflow. You get Evidence Analysis + Fraud Detection and a dated evidence trail.
Production in 4 weeks • Audit trail

Get Gavel live — without the Multi-Agent Systems drag.

Evidence-driven claims adjudication — analyzes disputes, images, and order data to determine validity, fraud risk, and severity.… Implementation: Evidence Pipeline → Fraud Detection → Adjudication Logic → Communication & Analytics.

Questions? Talk to a human • Responses in < 24h • Human signs every critical step

Evidence + audit trail•Azure AI Foundry + LangChain•Deterministic — LLM only for rationale