Claims Intake Agent
Extracts structured claim facts from documents and photos
Tool: Document extraction · Source: Claim submissions and imagery
Industry
Insurance AI agent operations
Claims intake, policy documents, exposure mapping and fraud review
Operational layers in view
Claim documentsPolicy recordsExposure mapFraud signalsAdjuster review
Claims Intake Agent
Policy Coverage Agent
Damage Assessment Agent
Exposure Agent
Fraud Agent
Adjuster Support Agent
Node activity below is a demonstration using sample insurance data.
Coverage decisions, settlements and payments are never issued autonomously. Every claim outcome requires an authorised adjuster decision.
Ask the live agents
Describe the problem in your own words. We classify the industry, assign specialist agents, and you watch the workflow run and produce the visual deliverables — maps, dashboards, charts, tables and reports — instead of one block of text.
Demo mode uses clearly labelled sample data — never presented as verified.
Assigned specialists
Insurance
- Claims Intake Agent
- Policy Coverage Agent
- Damage Assessment Agent
- Exposure Agent
- Fraud Agent
- Adjuster Support Agent
Deliverables planned
Workflow diagram
Annotated document
Data tables
Annotated imagery
Interactive map
Pauses for human approval
Adjuster approval
Coverage decisions, settlements and payments are never issued autonomously. Every claim outcome requires an authorised adjuster decision.
Workflow the run follows
- 1. User request
- 2. Requirement validation
- 3. Industry classification
- 4. Specialist agent selection
- 5. Data and file collection
- 6. Tool authorisation
- 7. Execution plan
- 8. Multi-agent collaboration
- 9. Analysis or generation
- 10. Quality validation
- 11. Human approval
- 12. Visual deliverables
- 13. Monitoring and audit
Demo
Insurance AI agent operations centre
Workflow: Claim received → Monitor claim · Run pending · started — · elapsed 0s
Active agents
0
Tasks running
0
Completed steps
0/9
Tool calls
0
Human approvals
1
Warnings
0
Success rate
0%
Est. completion
36s
Active agents
Pending
Policy Coverage Agent
Matches the claim against policy terms and endorsements
Tool: Document extraction · Source: Claim submissions and imagery
Damage Assessment Agent
Estimates damage severity from imagery
Tool: Document extraction · Source: Claim submissions and imagery
Exposure Agent
Maps portfolio exposure around the event
Tool: Document extraction · Source: Claim submissions and imagery
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch insurance agents work through the workflow with sample data.
Demonstration using sample data — no real client operation is shown. Sign in and connect an approved AI provider and authorised industry data source to activate live execution.
Insurance specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Claims Intake Agent
Extracts structured claim facts from documents and photos
- Inputs:
- Claim submissions and imagery, Policy and endorsement records, Hazard and exposure layers
- Tools:
- Document extraction, Damage vision model, Exposure scoring
Approval required
Policy Coverage Agent
Matches the claim against policy terms and endorsements
- Inputs:
- Claim submissions and imagery, Policy and endorsement records, Hazard and exposure layers
- Tools:
- Document extraction, Damage vision model, Exposure scoring
Autonomous
Damage Assessment Agent
Estimates damage severity from imagery
- Inputs:
- Claim submissions and imagery, Policy and endorsement records, Hazard and exposure layers
- Tools:
- Document extraction, Damage vision model, Exposure scoring
Autonomous
Exposure Agent
Maps portfolio exposure around the event
- Inputs:
- Claim submissions and imagery, Policy and endorsement records, Hazard and exposure layers
- Tools:
- Document extraction, Damage vision model, Exposure scoring
Approval required
Fraud Agent
Scores fraud indicators with reasons
- Inputs:
- Claim submissions and imagery, Policy and endorsement records, Hazard and exposure layers
- Tools:
- Document extraction, Damage vision model, Exposure scoring
Approval required
Adjuster Support Agent
Prepares the adjuster decision pack
- Inputs:
- Claim submissions and imagery, Policy and endorsement records, Hazard and exposure layers
- Tools:
- Document extraction, Damage vision model, Exposure scoring
Solution blueprint
Operational context
Industry challenges
- Operational data for insurance is spread across disconnected systems
- Analysis-heavy work is repeated manually every cycle
- Risks and exceptions are found late
- Reporting and audit overhead grows with volume
Solution patterns
Recommended AI solutions
- Specialist agents that own one step of the workflow each
- Grounded retrieval over your permitted systems only
- Predictive scoring with explainable reasons
- Approval-gated execution into your systems of record
Data readiness
Required data
- Claim submissions and imagery
- Policy and endorsement records
- Hazard and exposure layers
- Loss history
Controlled process
Example workflow
- Claim received
- Validate documents
- Extract claim facts
- Check coverage
- Assess damage
Connection points
Integration opportunities
- Policy administration
- Claims management
- GIS hazard services
- Payment systems
Measure after baseline
Expected outcomes
- Analysis cycle time
- Manual review effort
- Decision auditability
- Earlier risk detection
Governance controls
Security and compliance
- Row-level security scopes every read to the requesting organisation
- Retrieval is limited to documents the user may already open
- Every agent action, tool call and approval is written to the audit log
- Sensitive steps pause until a named human approves them
Inputs, integrations and outputs
Inputs
- • Claim submissions and imagery
- • Policy and endorsement records
- • Hazard and exposure layers
- • Loss history
Integrations
- • Policy administration
- • Claims management
- • GIS hazard services
- • Payment systems
Outputs
- • Claim decision pack (PDF)
- • Portfolio exposure map (GeoJSON, CSV)
- • Fraud indicator report (PDF, CSV)
Business outcomes to baseline and measure
Analysis cycle time
Target and measurement method agreed during discovery.
Manual review effort
Target and measurement method agreed during discovery.
Decision auditability
Target and measurement method agreed during discovery.
Earlier risk detection
Target and measurement method agreed during discovery.
No performance result is promised before a baseline is measured. Targets are agreed for each engagement and reported against real delivery data.
How This AI Agent Works
The process the insurance agent team follows, from trigger to approved deliverable.
Compliance review workflow
From a policy or transaction to a classified, audited compliance decision.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. Policy or transaction: Trigger. A record, policy change or transaction enters review.
- 2. Retrieve applicable rules: Data source. Load the rule set that applies to this record type.
- 3. Validate evidence: Validation. Check that required evidence exists and is current.
- 4. Detect exceptions: AI agent. List breaches with the rule reference for each.
- 5. Risk classification: Decision. Assign severity and required response time.
- 6. Compliance review: Human approval. A compliance officer approves or rejects the recommendation. Human approval required.
- 7. Store audit record: Action. Persist decision, reviewer, timestamp and evidence.
- 8. Compliance report: Output. Exception register, classification and audit trail.
- 9. Monitor remediation: Monitoring. Track open exceptions to closure.
Approval gate: Compliance review. Deliverable: Report.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| compliance-monitoring-agent | 9 | Yes. Policy: always | Report | Available | Available (signed in) | Available via Studio · requires ai_provider | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Relevant case study
Illustrative case study
Wastewater network GIS for a national water utility
A wastewater network needed utility-GIS structuring, mapping and quality-controlled network intelligence for daily operations.
View all case studiesReady to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.