Underwriting AI, claims automation, and risk scoring. In production, not pilot.
We build the decision systems that price risk, process claims, and catch fraud.
Insurance runs on decisions: pricing risk, processing claims, detecting fraud. Most carriers still make these decisions with rules written a decade ago. We build AI systems that score risk in real-time, automate 80% of claims processing, and catch fraud patterns human reviewers miss.
Why AI in Insurance is different.
Insurance is one of the most data-rich industries on the planet, and one of the slowest to act on it. Carriers sit on decades of claims history, actuarial data, and customer behavior signals. Fewer than 15% of insurers have AI in production beyond pilot stage. The rest are still processing FNOL by hand.
The carriers that have deployed AI for claims automation are seeing 20-50% reductions in claims cycle time and meaningful drops in loss ratios. The ones that haven't are watching combined ratios climb while insurtechs take their most profitable segments.
The hard part is not building a fraud model or an underwriting scorer. It is getting that model approved by compliance, integrated with a 20-year-old policy admin system, and trusted by adjusters who have done this work manually for decades. That is the execution gap we close.
The Problems That Slow You Down
Claims processing backlog keeps growing
Manual review of every claim. Adjusters spend 60% of time on data entry and document chasing, not decision-making.
Underwriting accuracy hasn't improved in years
Same actuarial tables, same risk factors. New data sources exist but aren't wired into the pricing model.
Fraud detection catches too little, flags too much
Rule-based systems flag 20% of claims as suspicious. 95% of those are false positives. Real fraud slips through.
Customer portals from the CD-ROM era
Policyholders can't check claim status, update coverage, or file digitally. Every interaction requires a phone call.
Systems for Insurance
Underwriting AI
ML models that score risk using traditional and alternative data. Pricing accuracy up, loss ratios down.
View service →Claims Automation
IDP that reads FNOL documents, extracts key fields, routes to the right adjuster, and auto-approves straightforward claims.
View service →Fraud Detection
Multi-signal fraud models that catch patterns across claims, claimants, and providers, catching what rules miss.
View service →Compliance & Governance
Regulatory reporting, model risk management, and audit trails that satisfy state insurance regulators.
View service →These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.
What We Build in Insurance
End-to-End Claims Intake Agent
VP of Claims OperationsFNOL via call centers + email. Simple claims wait 2-3 days, 15-25% longer cycle times.
LLM+RAG claims agent ingests omni-channel FNOL (voice, forms, emails, photos), extracts fields via multimodal LLM, checks coverage, auto-routes/approves low-severity via API.
Claims Document Understanding
Head of Claims TransformationAdjusters manually parse police reports, estimates, medical reports. 30-60 min per complex claim, 15-20 files/day max.
Multimodal LLM pipeline covering OCR, key-value extraction, cross-document consistency checks, validation report + decision rationale.
Claims Fraud Scoring (Multimodal)
Director of SIUSIU sees thousands of alerts/month, 80-90% false positives; fraud consumes 5-10% of claims costs.
Gradient-boosted + deep models on claims history + CV on damage photos + graph features across claimants/providers/policies.
Underwriting Submission Triage
Head of Commercial UnderwritingCommercial P&C underwriters get unstructured submissions via email. 30-60 min per submission.
NLP models extract risk attributes from PDFs/emails, enrich with geospatial/catastrophe data, compute risk/priority scores for routing.
Property Risk Scoring from Imagery
VP of Property UnderwritingManual inspections miss roof degradation, vegetation risk; mispriced policies, catastrophe surprises.
CNN on satellite/aerial/street imagery + geospatial layers (flood, wildfire), producing property risk scores for pricing workflows.
Policy Issuance Document Automation
Head of Policy AdministrationPolicy issuance and endorsements take 2-5 days manual drafting; ties up ops staff.
LLM document generator from approved clause libraries, validates against rating data, pushes to policy admin with regulatory checks.
Customer Retention & Lapse Prediction
VP of Customer Analytics10-20% policy lapse/attrition; limited proactive outreach across billing/service signals.
XGBoost on policy tenure, payment patterns, coverage changes, claims history, generating lapse propensity scores that trigger retention workflows.
CAT Event Impact Triage
Head of Catastrophe RiskAfter hurricanes/wildfires/floods, tens of thousands of simultaneous claims; can't prioritize outreach or reserving.
Weather feeds + satellite imagery + portfolio exposure data, producing property-level damage estimates and prioritized contact lists.
Building the system is half the job. Growing the business around it is the other half.
How We Grow Insurance Brands
AEO for Policy Comparisons
Digital Marketing DirectorInsurers lose 50% of "best car insurance for young drivers" to ChatGPT summaries; zero-clicks drop quote starts 35%.
Schema guides (coverages, discounts, claims), compliant FAQs, Perplexity monitoring to quote funnels.
LinkedIn Ads for B2B Commercial Lines
Head of Broker MarketingLinkedIn CPL $45-65 for brokers; under 12% MQL rate.
ICP ads (risk managers) to webinars, HubSpot scoring, nurture for RFP readiness.
HubSpot Lead Scoring for Life Insurance
VP Marketing Ops4K leads/month, 70% unqualified; no intent signals for advisors.
HubSpot compliant scoring (engagement, life events), sequences, Salesforce sync.
AI Ads for Quote Generation
Performance Marketing LeadGoogle CPC $10-20 for "home insurance quote" with 2.5% conversion; absent from Gemini.
Gemini/ChatGPT sponsored + Google PMax, direct quote CTAs.
How We Work With Insurance
AI Systems & Agents
LLMs, agent chains, and intelligent workflows, wired into your ops, not sitting in a sandbox.
Learn more →Intelligent Automation
The repetitive judgment calls your team makes 200 times a day? We build systems that handle them.
Learn more →Machine Learning Solutions
Prediction systems that run 24/7. Not proof-of-concepts gathering dust.
Learn more →Security & Compliance
SOC 2, ISO 27001, HIPAA, AI governance: audits that close enterprise revenue, not just check boxes.
Learn more →Frequently asked questions
For straightforward claims (60-80% of volume), yes, with auto-adjudication and human oversight. Complex claims get routed to the right adjuster with AI-extracted context.
Every model ships with explainability reports, bias testing, and documentation. We work with your compliance team from sprint one, not after deployment.
Historical claims data, policy data, and loss data. We assess data readiness in week one and fill gaps with third-party enrichment where needed.
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Related Answers
How can AI speed up insurance claims processing?
From weeks to hours. Document extraction, damage assessment, fraud scoring, and auto-adjudication. Here's what the full stack looks like and what it costs.
4 min readanswerHow do I know if my data is ready for AI?
If you can't answer "where is our customer data?" in under 30 seconds, it's not ready. Here are the 5 signs your data needs work before AI can help.
3 min readYour claims backlog grows every week. We build systems that shrink it.
Start with a claims and underwriting diagnostic. We'll identify automation opportunities and build a phased implementation plan.