Insurance AI agents built for claims, underwriting, and risk workflows.

Instantly ingests and classifies unstructured data from disparate sources — emails, handwritten claim forms, photographs of damage, police reports, PDFs. Uses machine learning-based OCR and layout detection to extract critical entities: policyholder information, dates of loss, granular damage descriptions. Converts unstructured noise into structured, database-ready entries automatically.

Evaluates the severity and complexity of every incoming claim using a mathematical scoring model. Cross-references claim details with policy limits, deductibles, and specific endorsements. Routes low-complexity claims to straight-through automated processing and complex claims to the most appropriate human adjuster based on their expertise.

For routine, low-complexity claims autonomously executes the entire settlement workflow. Verifies coverage, calculates payout, generates settlement documentation, and issues payment — all without human review. Settlement is completed within hours of claim submission.

Generates empathetic, personalized claims status communications to policyholders throughout the claims lifecycle. Keeps claimants informed at every stage without requiring human drafting. Handles multi-channel communication — email, SMS, portal messages.

After a claim is settled, this agent analyzes the claim file to identify whether a third party bears legal or financial responsibility. Flags subrogation opportunities automatically and initiates and manages the pursuit process, drafting demand letters and tracking responses.

Builds comprehensive risk profiles by cross-referencing ingested submission data against proprietary guidelines. Pulls data from satellite imagery, IoT telematics, vehicle records, weather databases, and registries. Generates a clean, structured, underwriter-ready summary brief with flagged exceptions.

Uses customer/broker input with underwriting rules to calculate premium and coverage options. Generates a fully personalized quote in real time and enables immediate policy binding including payment confirmation and documentation generation.

Analyzes every incoming claim against historical patterns, public records, and external data. Detects subtle inconsistencies using NLP and uses document image analysis to spot forged documents or fabricated records. Flags high-risk claims for Special Investigative Unit (SIU) review with full context.

Uses OCR and machine learning to verify identity documents submitted during onboarding or claims. Detects synthetic identities and analyzes images for signs of digital alteration. Cross-references against third-party verification databases.

Conducts interactive natural language conversations with customers. Answers policy questions, explains coverage, provides claims status, and generates personalized quotes. Processes FNOL and handles policy changes natively.

Processes requests to add drivers, change addresses, update vehicles, or modify limits. Validates requested changes against rules, executes the endorsement in the policy admin system, and issues updated documentation automatically.

Tracks renewal dates and automatically generates quotes based on current risk and market pricing. Identifies policyholders at high risk of non-renewal, triggering targeted retention interventions like coverage reviews and personalized offers.

Monitors payment schedules, sending personalized reminders before due dates. Identifies non-pay cancellation risks early and initiates structured collections workflows for delinquent accounts, escalating only when required.

Handles the complete new customer onboarding workflow autonomously. Scans identity documents, conducts KYC checks against databases, completes full data entry into the administration system, and issues the policy and all onboarding documentation.

Operates as a real-time assistant during sales interactions. Surfaces personalized product recommendations, answers complex coverage questions instantly to avoid putting customers on hold, identifies cross-sell opportunities, and automates post-call CRM logging and follow-ups.

Automates the extraction of mortality data, historical claims info, and financial metrics from legacy systems. Reconciles discrepancies between systems and consolidates decades of actuarial data into normalized datasets.

Uses transparent machine learning models to compute pure premiums and optimize rate strategies. Combines risk forecasting with demand to balance accuracy and competitiveness. Deploys adjustments in seconds and returns quotes in milliseconds.