AI for Insurance
Claims, Underwriting & Fraud Detection

We build custom AI systems for insurance companies , from claims processing automation and fraud detection to AI underwriting and RAG-powered customer chatbots. Built with LangChain, LangGraph, and PyTorch.

LangGraph Agents
Fraud Detection
RAG Chatbots
LangChain
PyTorch
60–70%
Faster claims processing
LangGraph agents handle intake, extraction, and routing automatically.
3x
More fraud detected
PyTorch models catch fraudulent claims that manual review misses.
80%
Customer inquiries resolved by AI
RAG chatbot handles coverage questions and claims status 24/7.
What We Build

AI capabilities built for insurance

Every system is designed for accuracy, compliance, and explainability , the non-negotiables in insurance AI.

Claims Processing Automation

LangGraph agents that handle the full claims workflow , intake, document extraction, fraud screening, policy matching, settlement calculation, and approval routing. Reduces claims processing time by 60–70% while maintaining accuracy and compliance.

LangGraphDocument AIPolicy MatchingAuto-Settlement

AI Fraud Detection

PyTorch models that analyze claim patterns, claimant history, network relationships, and anomalous signals to score each claim for fraud risk. Trained on your historical data, these models catch 2–3x more fraudulent claims than manual review.

PyTorchAnomaly DetectionNetwork AnalysisRisk Scoring

AI Underwriting & Risk Assessment

LangGraph agents that orchestrate the full underwriting workflow , data collection, risk assessment, compliance checks, premium calculation, and policy generation. Reduces underwriting time from days to hours with full audit trails.

LangGraphRisk ModelsPremium CalculationAudit Trail

RAG Customer Service Chatbot

RAG-powered chatbot trained on your policy documentation, coverage details, and FAQs. Customers get instant, accurate answers about their coverage, claims status, and procedures , grounded in their actual policy. Built with LangChain and Qdrant.

RAG SystemLangChainQdrantPolicy Q&A

Document Processing Pipeline

NLP pipelines that extract structured data from claim forms, medical reports, police reports, and supporting documents automatically. Uses Hugging Face models fine-tuned on insurance text to identify fields, validate completeness, and flag inconsistencies.

NLPHugging FaceForm ExtractionData Validation

Compliance & Regulatory AI

AI systems that monitor regulatory changes, check policy language against compliance requirements, and generate audit-ready reports. Every AI decision includes explainability features and full logging for regulatory review.

Regulatory MonitoringExplainabilityAudit LoggingCompliance Reports
FAQ

Common questions

AI claims automation uses LangGraph agents to handle the full claims workflow , intake, document extraction, fraud screening, policy matching, settlement calculation, and approval routing. The agent processes claim documents using NLP, cross-references policy terms, and routes straightforward claims for automatic settlement while flagging complex cases for human review.
Yes. We build fraud detection models using PyTorch that analyze claim patterns, claimant history, network relationships, and anomalous signals to score each claim for fraud risk. These models are trained on your historical claims data and improve over time. Fraud detection AI typically catches 2–3x more fraudulent claims than manual review.
A RAG chatbot for insurance connects an LLM to your policy documentation, coverage details, and FAQs. Customers get instant, accurate answers about their coverage, claims status, and procedures , grounded in their actual policy, not generic responses. Built with LangChain and Qdrant, it handles 70–80% of customer inquiries without agent involvement.
LangGraph agents orchestrate the full underwriting workflow , collecting applicant data, running risk assessments, checking compliance rules, calculating premiums, and generating policy documents. This reduces underwriting time from days to hours while maintaining accuracy and full audit trails for regulatory compliance.
Yes. We build NLP pipelines that extract structured data from claim forms, medical reports, police reports, and supporting documents automatically. Using Hugging Face models fine-tuned on insurance text, the system identifies relevant fields, validates data completeness, and flags inconsistencies , eliminating manual data entry.
Insurance AI systems must comply with state and federal insurance regulations, fair lending laws, and data privacy requirements. We build systems with explainability features , every AI decision includes a rationale traceable to input data , and audit logging for regulatory review. We work with your compliance team to ensure all systems meet applicable requirements.
Get Started

Ready to build your insurance AI system?

Schedule a free consultation. We will assess your workflows, discuss compliance requirements, and outline a delivery plan.