Vision-Guided Agents for Insurance Data Extraction
Underwriters had to manually navigate 15+ legacy government portals to verify policyholder data, causing a massive bottleneck in the quote-to-bind funnel.
The Problem
Insurance underwriting requires verification—lots of it. An InsurTech startup’s underwriters were spending hours each day manually navigating government portals: DMV records, property databases, licensing boards. Each portal had different UIs, different login flows, different data formats. Traditional Selenium scripts broke constantly as these legacy sites updated.
The Architecture
flowchart TB
subgraph request [Quote Request]
Application[Policy Application]
DataNeeds[Verification Checklist]
end
subgraph orchestration [Agent Orchestration]
Router[Portal Router]
Navigator[Vision Navigator]
Extractor[Data Extractor]
end
subgraph browser [Browser Layer]
Playwright[Playwright Instance]
Screenshot[Screenshot Capture]
DOM[DOM State]
end
subgraph inference [Local Inference]
Llama[Llama 3.1 8B]
Vision[Vision Analysis]
end
subgraph output [Verified Data]
Structured[Structured Output]
Confidence[Confidence Scores]
end
Application --> Router
DataNeeds --> Router
Router --> Navigator
Navigator --> Playwright
Playwright --> Screenshot
Screenshot --> Vision
Vision --> Llama
Llama --> Navigator
Playwright --> DOM
DOM --> Extractor
Extractor --> Structured
Extractor --> Confidence Vision-Guided Navigation
The breakthrough: agents that see the page like humans do.
- Portal Router: Determines which government portals need to be accessed based on the policy application
- Vision Navigator: Takes screenshots of each page state and uses LLM reasoning to determine next actions—no CSS selectors, no XPath, just “click the blue ‘Search’ button”
- Data Extractor: Parses the final result pages using Pydantic models for structured output
When a portal redesigns their UI, the agent adapts. No code changes required.
Tech Stack
- Playwright — Headless browser automation
- PydanticAI — Structured data extraction with validation
- Llama 3.1 8B — Local inference for privacy (policyholder data never leaves the VPC)
- AWS Lambda — Serverless execution at scale
The Impact
| Metric | Before | After |
|---|---|---|
| Daily Quote Capacity | 50 | 250+ |
| Script Breakage Rate | 15%/month | 0% |
| Manual Verification Time | 45 min/quote | 3 min/quote |
| Portal Coverage | 8 portals | 15+ portals |
The system handles portal updates gracefully—a redesigned DMV site that would have broken Selenium scripts for days is navigated correctly on the first attempt.