Automations
This pillar covers claims workflows where specialized agents classify incidents, analyze visual evidence, estimate damages, coordinate communications, and route settlements or payouts through regulated approval paths. The content should frame these pages as blueprints for custom claims platforms that reduce handling cost, accelerate resolution, and preserve auditability across insurers, TPAs, and digital claims operations.
This foundational page details the end-to-end architecture for a custom multi-agent claims system, where specialized agents orchestrate FNOL, evidence analysis, coverage checks, and settlement routing. It explains how to build a production-grade workflow that reduces handling cost by 30-50%, accelerates resolution cycles, and maintains a defensible audit trail across insurers and TPAs. The implementation blueprint covers agent coordination with LangGraph, integration with core policy systems, and the governance controls required for regulated, high-volume claims operations.
This page covers the custom workflow for automatically classifying incoming claims by type, severity, and complexity, then routing them to the appropriate adjuster, vendor, or straight-through processing lane. It details the architecture for combining NLP on claim descriptions, rules-based triage logic, and real-time integration with workforce management systems to eliminate manual sorting delays. The build focuses on reducing assignment lag from hours to seconds, improving adjuster utilization, and ensuring complex cases receive immediate expert attention.
This page explains the custom multi-agent workflow for continuous fraud screening, where agents cross-reference claims against internal databases, external intelligence feeds, and behavioral patterns to flag suspicious activity. It details the architecture for ingesting disparate data sources, applying graph analytics to detect collusion networks, and routing high-risk cases to SIU with a compiled evidence package. The implementation reduces false positives, accelerates investigation starts, and creates a scalable defense against first-party and organized fraud.
This page details the automation workflow where an agent retrieves policy documents, interprets coverage terms, endorsements, and exclusions, and validates the claim against them at the point of FNOL. It explains the integration architecture with policy administration systems (e.g., Guidewire, Duck Creek), the use of RAG for document understanding, and the logic for escalating coverage questions. The build eliminates manual lookup errors, prevents unnecessary claim setup, and provides instant, accurate coverage guidance to customers and adjusters.
This page covers the custom workflow for ingesting and de-duplicating claim submissions that arrive via app, web, call center, email, and partner APIs into a single, actionable case file. It details the architecture for identity resolution, data normalization, and conflict detection across channels, ensuring no claim is missed or duplicated. The implementation reduces administrative reconciliation work, improves data quality for downstream processing, and creates a unified customer view regardless of entry point.
This page explains the custom workflow where computer vision agents analyze customer-submitted photos or videos to detect parts, assess damage severity, and generate a preliminary repair estimate. It details the integration with parts databases and labor guides, the confidence scoring for automated vs. human review, and the handoff to network repair shops. The build accelerates estimates from days to minutes, reduces reliance on field appraisals for simple claims, and improves estimate accuracy through consistent algorithmic assessment.
This page details the workflow for processing drone-captured imagery of roofs, exteriors, and large properties to autonomously identify hail damage, wind uplift, or fire patterns. It covers the architecture for geospatial data ingestion, damage segmentation models, integration with estimating platforms like Xactimate, and the routing of detailed inspection reports to adjusters. The implementation enables rapid CAT response, reduces dangerous ladder climbs, and provides precise, auditable damage documentation.
This page covers the custom automation for parsing unstructured repair estimates, mechanic invoices, and medical bills using OCR and LLM agents to extract line items, costs, and procedure codes. It explains the validation logic against negotiated rates and usual & customary fees, and the automated routing of exceptions for review. The build eliminates manual data entry, accelerates payment cycles, and ensures compliance with payment policies across thousands of vendor documents.
This page details the specialized workflow where forensic analysis agents examine submitted photos and videos for metadata inconsistencies, cloning, splicing, or other digital manipulation indicative of fraud. It covers the architecture for integrating detection models, correlating findings with other claim data, and escalating tampered evidence to investigators with a detailed analysis report. The implementation strengthens fraud defenses at the point of submission and creates a technical barrier against increasingly sophisticated image-based scams.
This page explains the workflow for automatically ingesting hyperlocal weather data (wind speed, hail size, precipitation) from third-party APIs and correlating it with the time and location of a property claim. It details the logic for validating weather-related perils, flagging claims that fall outside verified event parameters, and generating a weather verification report for the file. The build reduces investigation time for weather claims, helps prevent fraudulent claims for non-events, and supports accurate CAT loss modeling.
This page covers the custom workflow where conversational AI agents contact provided witnesses via SMS or voice, collect structured statements, and analyze narratives for consistency and key facts regarding liability. It details the NLP architecture for sentiment and contradiction detection, and the integration of summarized statements directly into the claim file. The implementation scales evidence collection beyond adjuster capacity, captures statements closer to the incident date for accuracy, and surfaces critical discrepancies early.
This page details the automation for programmatically querying law enforcement databases or portal APIs to retrieve accident reports, then using LLM agents to extract key facts: parties, vehicle details, citations, and officer narrative. It explains the workflow for matching reports to claims, highlighting potential liability indicators, and populating claim fields automatically. The build eliminates manual follow-up and data entry for police reports, shaving days off the investigation phase and improving data accuracy.
This page explains the custom, privacy-compliant workflow where monitoring agents scan public social media and web sources for posts related to a claim, claimant, or incident to identify activity inconsistent with claimed injuries or damages. It details the architecture for alerting logic, evidence preservation, and integration with investigation case management. The implementation provides a scalable way to uncover fraud signals that are invisible to internal systems, strengthening the evidentiary basis for claim decisions.
This page covers the workflow where analysis agents review completed liability assessments and payment details to automatically flag claims with clear third-party fault for subrogation recovery. It details the logic for determining recovery likelihood, preparing the initial demand package with relevant evidence, and assigning the case to recovery specialists or external counsel. The build ensures no recoverable claim is overlooked, accelerates the start of the recovery process, and improves overall loss ratio performance.
This page details the complex automation for managing the IME process in workers' comp or injury claims: selecting appropriate specialists based on injury type, scheduling appointments, sending records, collecting the report, and summarizing findings for the adjuster. It explains the integration with vendor networks, calendar systems, and document management to eliminate manual coordination. The implementation reduces IME setup time from weeks to days, ensures regulatory compliance, and frees adjusters for higher-value tasks.
This page explains the workflow where pricing agents synthesize damage assessments, coverage limits, state regulations, and historical settlement data to generate a defensible initial settlement offer. It details the rules engine and ML model architecture, the inclusion of explanatory rationale, and the secure presentation of the offer through customer portals or direct communication agents. The build standardizes offer calculation, reduces negotiation cycles, and empowers adjusters to settle low-complexity claims faster.
This page covers the mission-critical workflow for authorizing and issuing claim payments, where agents validate settlement approvals, check for liens or assignments, select the optimal payment method (ACH, check, virtual card), and trigger the transaction through integrated banking or payment platforms. It details the fraud and compliance checks, reconciliation triggers, and exception handling for failed payments. The implementation eliminates manual payment processing, reduces errors, and accelerates the final step in the claims lifecycle, improving customer satisfaction.
This page details the automation for screening every settlement payment against internal policies, state regulations (e.g., fair claims practices), and external databases (OFAC, etc.) before release. It explains the architecture of pre-payment approval gates, audit log generation, and the routing of flagged payments for legal or compliance review. The build institutionalizes regulatory adherence, minimizes the risk of fines or bad-faith allegations, and creates a bulletproof audit trail for every disbursement.
This page covers the workflow for managing payments to a network of contractors, repair facilities, and medical providers. Agents match completed work to estimates, validate invoices, apply network discounts, and initiate payment, while coordinating supplements and communicating status to all parties. It details integration with vendor management and estimating systems. The implementation reduces accounts payable overhead, strengthens network relationships through prompt payment, and provides full financial visibility into vendor spend.
This page explains the customer communication workflow where agents monitor claim milestones and automatically send personalized updates via SMS, email, or app notification based on customer preference. It details the integration with the claims core system to detect status changes and the logic for tailoring message tone and detail. The build dramatically reduces "where’s my claim?" calls, improves customer satisfaction scores (CSAT), and allows adjusters to focus on complex tasks rather than status inquiries.
This page details the workflow for deploying conversational AI agents that can answer common claim questions, explain processes, and collect information in dozens of languages, 24/7. It covers the architecture for integrating with the knowledge base and claim system, context management across conversations, and seamless handoff to human agents for complex issues. The implementation breaks down language barriers, scales support capacity without adding staff, and ensures consistent information delivery across a diverse customer base.
This page covers the workflow for intelligently requesting missing documents (e.g., driver's license, repair invoices) from claimants. Agents determine what's needed based on claim type, send personalized requests via the customer's preferred channel, and provide secure upload methods. It details the logic for tracking submissions, sending reminders, and alerting adjusters of delays. The build accelerates the evidence collection process, reduces repetitive follow-up tasks for staff, and improves the completeness of claim files.
This page explains the workflow where NLP agents analyze customer call transcripts, chat logs, and written communication in real-time to detect frustration, anxiety, or anger. It details the architecture for triggering immediate alerts to supervisors or specialized adjusters, along with context and recommended de-escalation steps. The implementation helps prevent customer churn and bad-faith complaints by ensuring vulnerable claimants receive timely, empathetic human intervention, improving both experience and risk outcomes.
This page details the coordination workflow where agents act as intelligent schedulers, finding mutually available times for inspections, repair drop-offs, rental car pickups, and medical evaluations. It covers integration with adjuster calendars, vendor booking systems, and rental company APIs. The build eliminates the back-and-forth calls and emails that delay repairs, improves resource utilization, and provides customers with a seamless, coordinated service experience.
This page covers the high-volume automation for CAT events, where agents segment affected policyholders by peril and damage level, then orchestrate personalized outbound communications with safety information, filing instructions, and expectation setting. It details the architecture for scaling to millions of messages, integrating with catastrophe modeling tools, and routing urgent cases for immediate human contact. The implementation manages customer anxiety at scale, reduces contact center overload, and demonstrates proactive care during crises.
This page details the specialized workflow for auto insurers where agents ingest telematics data (speed, braking, g-force) from the moments before a collision to automatically reconstruct the accident sequence and assess likely fault. It explains the integration with telematics provider APIs, the physics modeling involved, and the generation of a preliminary liability report. The build provides objective, data-driven fault assessment, reduces disputes, and accelerates resolution for telematics-enabled policyholders.
This page explains the workflow for property claims where agents ingest satellite imagery, weather data, and government fire/flood maps to automatically verify that a reported loss occurred within a documented catastrophic event area and assess the likely severity based on proximity. It details the geospatial analysis and the generation of a peril verification report for the adjuster. The implementation accelerates triage during large-scale events, helps prioritize the most severe claims, and adds a layer of objective verification against fraudulent claims for non-events.
This page covers the workflow for health claims where agents review medical bills and Explanation of Benefits (EOBs) against policy coverage, provider contracts, and standard coding practices (CPT, ICD-10) to identify overcharges, unbundling, or incorrect codes. It details the integration with claims adjudication engines and the routing of exceptions to clinical reviewers. The build automates a highly manual process, ensuring accurate payments, recovering overpayments, and controlling healthcare costs.
This page details the workflow for workers' compensation where an intake agent guides an injured employee through initial reporting, collects details about the injury and job demands, and immediately triggers a case file and alerts. Subsequent agents coordinate with medical providers, suggest modified duty options based on job descriptions, and track recovery milestones to prompt return-to-work planning. The implementation ensures rapid, compliant injury reporting and proactive disability management, reducing lost-time costs and improving employee outcomes.
This page explains the critical workflow for cyber claims where, upon a breach notification, agents instantly activate: collecting technical logs, assessing the scope of data exposure, triggering pre-approved incident response retainer agreements with legal and forensic firms, and coordinating communications with the insured. It details integration with security tools and vendor ecosystems. The build reduces the time between breach discovery and containment, mitigates loss severity, and provides structured support during a high-stress event.
This page is a technical blueprint for building the integration layer between a custom multi-agent claims workflow and legacy or modern policy admin systems (e.g., Guidewire, Duck Creek, SAP). It covers strategies for real-time and batch data sync, handling API limitations, and maintaining data consistency across systems. The implementation ensures the agentic workflow has accurate, up-to-date policy data to make decisions, making the automation viable within an existing enterprise tech stack.
This page details the workflow for bi-directional data synchronization between the claims automation layer and customer-facing systems (CRM like Salesforce, custom portals). Agents ensure all claim updates, communications, and documents are reflected in the portal in real-time, and portal submissions instantly trigger workflow steps. It covers the architecture for maintaining a single customer view. The build creates a cohesive digital experience, eliminates data silos, and empowers customers with self-service, reducing contact volume.
This page covers the operational workflow where agents manage the entire repair lifecycle: assigning jobs to network shops based on capacity and specialty, sending estimates, tracking repair status via shop management system integrations, and handling supplement approvals. It details the logic for automated assignment and exception escalation. The implementation reduces cycle time by streamlining coordination, ensures quality through preferred networks, and provides full visibility into the repair pipeline.
This page explains the workflow for instantly triggering roadside and rental services at the point of FNOL. Agents determine need based on accident details, locate available services via integrated APIs (e.g., Enterprise, AAA), book the service, and provide details to the claimant—all without adjuster intervention. It details the cost control logic and integration patterns. The build improves customer experience during a stressful event and controls ancillary claim costs through automated, policy-compliant service selection.
This page details the governance workflow where monitoring agents track every claim against regulatory and internal service level agreements (SLAs) for acknowledgment, contact, inspection, and payment. It explains the architecture for real-time dashboards, automated alerts to supervisors for at-risk claims, and the generation of compliance reports. The build proactively manages operational performance, helps avoid regulatory penalties for missed deadlines, and identifies process bottlenecks for continuous improvement.
This page covers the critical workflow for compliance and litigation readiness, where a dedicated logging agent records every decision, data access, communication, and state change within the claims workflow, along with the rationale from each AI agent involved. It details the immutable storage architecture, retrieval mechanisms, and integration with legal hold processes. The implementation creates a forensic-grade audit trail that satisfies internal audit, regulatory scrutiny, and e-discovery demands in litigation.
This page explains the workflow where analysis agents continuously monitor claims for characteristics that mandate special handling: high reserve amounts, specific injury types, attorney representation, or coverage disputes. It details the logic for immediate flagging, assignment to specialized units, and triggering of appropriate governance workflows. The build ensures high-exposure or high-risk claims never slip through standard processing lanes, mitigating financial and reputational risk through early expert intervention.
This page details the responsible AI workflow where monitoring agents analyze patterns in claim outcomes—assessment values, settlement amounts, denial rates—across demographic segments to detect potential unfair bias. It explains the statistical analysis, alerting to ethics committees, and the feedback loop to adjust underlying models or rules. The implementation proactively addresses fair claims handling regulations, protects brand reputation, and ensures equitable treatment of all policyholders.
This page covers the advanced workflow where predictive modeling agents score every new claim in real-time based on hundreds of structured and unstructured features, assigning a fraud probability. It details the integration of this score into the triage workflow to route high-likelihood claims for immediate SIU investigation and low-likelihood claims for straight-through processing. The build optimizes investigative resources, increases fraud detection rates, and reduces loss adjustment expense by focusing human effort where it's most needed.
This page explains the retention workflow where, after claim closure, agents analyze the claim experience and customer profile to determine retention risk. They then trigger personalized outreach—thank you messages, policy review offers, or loyalty discounts—via the customer's preferred channel. It details integration with marketing and policy systems. The build mitigates the high lapse rate post-claim, improves customer lifetime value, and turns a service moment into a retention opportunity.
This page details the workflow where agents analyze claim factors (disputed liability, specific injury types, claimant representation history) to predict the likelihood of the claim escalating to litigation. For high-risk claims, the workflow automatically triggers early intervention strategies: scheduling a mediation, assigning a senior adjuster, or preparing a pre-litigation settlement package. The build reduces legal defense costs by resolving disputes before suit is filed and manages the portfolio's litigation exposure proactively.
This page is the ultimate blueprint for straight-through processing, detailing the fully automated workflow for high-frequency, low-complexity claims (e.g., minor windshield repair, low-value property damage). It covers the orchestration of FNOL, coverage check, image-based damage assessment, automated payment, and customer communication without human touchpoints. The implementation architecture focuses on exception handling and fallback protocols. This build achieves the highest possible loss adjustment expense savings and instant customer satisfaction for a target claim segment.
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One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
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