Extract, Interpret, Transform, and Summarize Insurance Documents.
Purpose-built LLM technology for insurance documents that keeps business subject matter experts in control while eliminating manual data entry on even the most unstructured documents.
DEFINITION:
SortSpoke's Insurance Document LLM is an advanced artificial intelligence technology purpose-built for the insurance industry that combines machine learning, large language models, and human-in-the-loop verification. SortSpoke is specifically designed to process, interpret, and transform complex insurance documents with the right mix of AI speed and human review to maintain full control and confidence on every document.

Purpose-built for the insurance industry, SortSpoke's Insurance Document LLM extracts critical data, interprets complex policy language, transforms varied formats into standardized outputs, and generates actionable summaries — all with human oversight on every field and traceability back to the source.
This powerful combination enables underwriters to process more documents while keeping compliance and data quality intact.

Unlike general-purpose AI, SortSpoke's Insurance Document LLM is purpose-built for insurance-specific document processing, with a confidence score on every extracted field and your own reviewers on anything below the threshold your team sets.
Generic understanding without insurance-specific training
Limited traceability without source validation
Prone to hallucinations and inaccuracies without validation
Generic summaries missing insurance-specific context
Purpose-built for insurance with industry expertise
Every data point linked back to its source in the original document
Human-in-the-loop validation on every field below your confidence threshold
Insurance-specific summaries highlighting critical risk factors and policy details
SortSpoke runs two kinds of model. Its own PyTorch supervised models — trained separately for each customer, on that customer’s own labelled documents — handle field extraction, document splitting and table detection. Large language models run on AWS Bedrock for the cases a trained model does not cover, and every prediction still passes a human reviewer before it leaves the system.
The supervised models are trained per customer project, and the ground-truth labels come from that customer’s own reviewers working in the SortSpoke labelling interface. Every document, label, training record and trained model is tagged with the owning project and account, and no training pipeline or model-loading path crosses a project boundary. A model trained on one customer’s documents is not used to serve another.
Where SortSpoke calls a large language model, it does so through AWS Bedrock under terms that commit the provider not to retain prompts or responses for training, and not to use them to train or improve the underlying foundation models. SortSpoke does not train or fine-tune large language models on customer data.
Processing runs in AWS us-east-1, active across at least three availability zones. Documents in transit move over TLS 1.2 or higher; data at rest is encrypted with AES-256 in MongoDB Atlas and S3, with keys held in AWS KMS. Temporary working files sit on encrypted volumes and are deleted after use. AI training data, model weights and inference infrastructure sit inside the same SOC 2 and HIPAA third-party-audited scope as the rest of the platform — not in a separate, unaudited lane.
On account deletion, trained models and stored model weights are hard-deleted along with the customer’s documents, labels and training caches. Because the foundation models were never trained on customer data, there is nothing to purge from them.
Every extracted value carries provenance metadata pinning it to the page, region and character span of the source document it came from, and no AI output is treated as final until a human reviewer accepts it. Model choice is an architecture question; the review step is what decides whether a value reaches your systems.
So can I use ChatGPT and LLMs for Data Extraction?
The answer to this question really depends on your use case. If you’re looking to pull short snippets of information (e.g. name, account number, etc) from simple documents, and you can live with significant errors, then ChatGPT can do an acceptable job.”
Underwriters can quickly and accurately extract data in a fraction of the time without the need for manual work, speeding up processes and reducing time spent on tedious work.
Not all submissions are created equal. Your team can automate insurance submission triage with customizable triage rules and leverage intelligent prioritization to focus on the right risks that are most desired to bind.
Underwriters can efficiently extract data with a system that’s easy to use and ensures every piece of data is traceable—without ever compromising on accuracy, even for the most complex submissions.
Triage Demo
Gain complete control over your submission intake with SortSpoke's AI-powered triage. Our human-in-the-loop approach ensures your underwriters maintain full oversight while dramatically increasing efficiency.
Watch the demo to see how SortSpoke transforms chaotic submission inboxes into organized, actionable work for your underwriters.
Process an ACORD 125 in under 2 minutes, against 20–30 minutes by hand (SortSpoke internal benchmarks).
Ensure data your experts have checked by keeping them in the loop
Shorten turnaround times without adding headcount.
SortSpoke's Insurance Document LLM is fundamentally different from generic LLMs as it's purpose-built for insurance documents.
Unlike public models like ChatGPT, which is trained on general data across the internet, SortSpoke combines ML and LLM technologies specifically tuned to extract, interpret, and transform insurance documents with precision while maintaining complete traceability and human validation.
Benefits:
Unlike black-box AI systems, our approach combines specialized ML extraction with LLMs directly trained and continuously refined by industry subject matter experts who understand the nuances of insurance.
This expert-guided approach ensures the system interprets insurance language the way underwriters do, not how a generic AI would guess.
Benefits:
SortSpoke's Insurance Document LLM maintains traceability by creating a permanent link between every extracted data point and its source location in the original document.
This comprehensive audit trail allows underwriters to instantly verify any information by clicking directly to the source, maintaining complete transparency and compliance. All actions, including extractions, validations, and transformations, are logged for regulatory and audit purposes.
Benefits:
SortSpoke's Insurance Document LLM processes virtually any insurance document type, including submissions, applications, policies, endorsements, loss runs, emails, schedules, SOVs, broker slips, and supplementary forms.
SortSpoke handles multiple file formats including PDFs (both text-based and scanned), Word documents, Excel spreadsheets, emails, and images, all without requiring templates or predefined structures.
Benefits:
SortSpoke's Insurance Document LLM can be implemented in your existing workflows in just days, not months. Our configurable API and pre-built connectors enable quick integration with your current systems with minimal IT involvement. The solution begins delivering value immediately with no lengthy training period—it adapts to your specific documents and extraction needs through continuous learning with every interaction.
Benefits:
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