A life underwriting file holds the application, prescription history, lab results and physician records, plus financials for larger cases. Underwriters see each risk signal beside the page it came from.
LandingAI transforms documents into highly accurate, verifiable, structured data so teams can reliably automate document-intensive workflows.
An attending physician statement arrives as scanned pages of typed notes, handwritten annotations, charts and lab results. Munich Re notes that its length and scan quality can drive review time more than the medical case itself.2
Labs, cardiograph vendors, prescription databases and state motor vehicle agencies each format results their own way, so underwriters meet a new layout with almost every report.
Tobacco use, income and net worth on the application are checked against prescription history, labs and financial records. New York's insurance regulator expects carriers that use AI or external data in underwriting to show how that data relates to risk.3
The same Parse call reads a life insurance application, a prescription history report and an attending physician statement. Every document type uses one set of APIs, with zero templates to build and zero models to train or fine-tune.
A new form, issuer or document type works on its first call, with nothing to configure.
If an issuer redesigns a form or a new version arrives, no pipeline changes are needed.
Upload a sample in the Playground; agentic reasoning will immediately understand it.
Classify labels each document as it arrives, so prescription histories, lab reports, EKGs and records each go to the schema built for them.
Classify API →Parse reads scanned records, handwriting, charts and lab tables into structured Markdown, page by page.
Parse API →Section takes the Parse output of a long physician statement and builds a table of contents, so encounters, lab results and imaging reports can be found by heading.
Section API →Extract returns the fields your rules engine reads, such as fills, intervals and test results, as JSON that matches your schema.
Extract API →Ground maps each extracted value to the block, page and bounding box it came from, so an underwriter checks a flagged result on the original record before rating the case.
Ground docs →Trusted, high-volume processing of the documents involved in life insurance underwriting requires handling diverse document types and layouts. A prescription history report is very different from an attending physician statement, which is different from a Schedule K-1. The pipeline must provide high accuracy and consistent outputs across all of them.
Our agentic approach is able to reason over each document type and return structured hierarchical Markdown and JSON outputs that downstream systems can rely on.
Each document below links to a worked example. The documents and schemas are available in full on GitHub and you are welcome to use them for your own testing.
| Field | Source document | Used for |
|---|---|---|
| Face amount, tobacco use, earned income and net worth | Life Insurance Application | Evidence requirements and rate class |
| Drug, strength, fill date and prescriber | Prescription History Report | Accelerated path eligibility |
| Convictions, points and suspensions | Motor Vehicle Report | Risk class |
| Charges and dispositions | Criminal Background Check | Eligibility |
| Diagnosis, treatment and dates under care | Attending Physician Statement | Impairment rating |
| Test name, result and reference range | Lab Report | Rating debits and credits |
| Heart rate, interval measurements and interpretation | EKG Report | Cardiac rating |
| Impression and primary diagnosis | Medical Imaging Report | Impairment rating |
| Adjusted gross income | Form 1040 | Financial justification |
| Net profit or loss | Schedule C | Self-employed income justification |
| Ordinary business income | Schedule K-1 | Owner income justification |
| Net income | Business Profit and Loss Statement | Key person and buy-sell cover amounts |
| Ending portfolio value | Investment Statement | Net worth justification |
| Face amount, risk class and initial premium | Life Insurance Policy | Issued terms match the decision |
| Subject name, sanctions list and programs | Sanctions Screening | Issue clearance |
Underwriting leaders track three things: how fast a case reaches a decision, what evidence review costs and how well each rating holds up to an audit.
Applicants and agents wait on the slowest piece of evidence. Records and lab results read in minutes shorten full underwriting, and more cases reach a decision while the applicant is still committed.
The length and scan quality of an attending physician statement can drive review time more than the medical case itself. Extracted, grounded fields let underwriters start from the findings and open the record only where a value needs a closer look.2
Post-issue audits and regulators ask why a case received its rating. Each extracted value carries its page and location, so the evidence behind a rating can be shown on the original record.
The best accuracy on the difficult documents other tools get wrong, such as messy scans, complicated tables, checkboxes and handwriting.
Every answer cites its sources. Values come back with an audit trail (page, bounding box, character range) so a human can review them.
Applies multi-step reasoning, and plans before it acts. Handles documents it has never seen before and adapts to variations.
Composable APIs that drop into any tech stack and can be called from anywhere. Easy to build on and embed in your pipelines and products.
Cost-competitive rates with complete price transparency. Spend controls before you run, and every job reports its exact cost.
Built for production and scale from day one. Fully observable and monitored, running securely in our cloud or yours.
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