PDF Reader: Automated Quote Ingestion for Specialty Insurance

Industry: Insurance

Service: AI-Powered Document Extraction

Timeline: 6 Weeks

PDF Reader: Automated Quote Ingestion for Specialty Insurance

Team: Tempered AI, in partnership with Computomic

May 14, 2026

Executive Summary

Synapse operates in a high-volume specialty insurance environment where carrier quotes arrive daily as PDFs, each with different formats, structures, and page counts. Their team was extracting data manually, creating bottlenecks that were impossible to scale. Tempered AI built a cloud-hosted LLM extraction tool that reads incoming quotes automatically, surfaces the key fields for agent review, and handles carrier variability through a flexible configuration system. The result is 85% extraction accuracy at roughly 150x lower cost than comparable platforms on the market.

About the Client

Synapse is a fast-growing specialty insurance firm that manages a high volume of carrier quotes across a diverse range of insured risks. Their operations team receives PDFs daily from numerous carriers, each with different formats, terminology, and page counts ranging from 5 to 200+ pages, making manual data extraction a significant operational challenge.

The Challenge

Synapse receives a high volume of insurance quotes from numerous carriers, each covering a distinct set of insured risks and arriving as PDFs ranging from 5 to 200+ pages. Extracting the relevant data manually was slow, repetitive, and error-prone. The process put a real strain on operations and created bottlenecks that were hard to scale around.

Volume and format variability

Each carrier sends quotes in a different format. Some are 5 pages, others exceed 200. The structure, terminology, and layout vary significantly across carriers and lines of business, making any standardized manual process unreliable at scale.

Manual extraction was a bottleneck

Agents were spending significant time reading through PDFs and manually entering key fields into the system. The work was repetitive, error-prone, and impossible to scale without adding headcount.

No existing automation

There was no tooling in place to handle document ingestion. Any solution had to be accurate enough for a regulated insurance environment, flexible enough to handle format variability, and practical enough for agents to adopt without friction.

What We Built

A cloud-hosted LLM extraction tool that reads incoming PDF quotes, automatically surfaces the key fields, and presents them to the agent for review before anything gets submitted. The workflow keeps the agent in control while eliminating the manual reading and data entry work.

Key Features

Human-in-the-loop design

The agent uploads a quote, the tool extracts the key-value pairs and surfaces them in a clean interface. The agent reviews, corrects if needed, and confirms. Accuracy stays high without removing the agent from the process.

Hierarchical configuration for carrier variability

The system uses default settings layered with carrier and line-of-business-specific overrides to handle format variation across providers. The LLM prompt, model parameters, and PDF processing settings are all stored as version-controlled YAML files. Onboarding a new carrier requires no changes to core logic.

Intelligent page filtering

For documents longer than 20 pages, the pipeline filters out irrelevant pages such as legal boilerplate and blank forms before sending content to the LLM. In practice this reduces the content passed to the model to around 7% of pages beyond page 20, keeping costs low, processing fast, and content well within context window limits.

Technical Highlights

The tool is deployed as an Azure Function with CI/CD connected to Synapse's GitHub repository, so new versions go live automatically on each commit. The hierarchical YAML configuration system means carrier-specific logic lives outside the core codebase, keeping the system maintainable and easy to extend. The page filtering step is where the cost efficiency comes from: by stripping irrelevant content before it reaches the LLM, the system processes only what matters, which keeps token usage low and response times fast regardless of document length.

The Result

Key-value pairs extracted with 85% accuracy across production batches

Processing cost of approximately $0.20 per 1,000 pages

Comparable platforms charge $30-40 per 1,000 pages, making this solution roughly 150x more cost-effective

In practice, Synapse stays within their cloud provider's free tier and incurs no direct usage cost

New carriers and formats can be onboarded without touching core system logic

Why It Works

The accuracy comes from close collaboration with Synapse during development. Rather than relying on a generic extraction approach, the team worked directly with their team to build document and domain knowledge into the configuration layer. The result is a system calibrated to the structure and terminology of their specific quote types, not document extraction in general.

Spending too much time on manual document processing?

We can build extraction tools calibrated to your documents, your formats, and your workflows.