An auto loan funding packet holds the retail installment contract, odometer disclosure and title, plus the stips: pay stubs, bank statements, bills and proof of insurance.
LandingAI transforms documents into highly accurate, verifiable, structured data so teams can reliably automate document-intensive workflows.
A dealer's packet stacks the contract, title paperwork and insurance with the buyer's pay stubs and bills. Lender checklists name a dozen documents.3
The buyer's name, address, VIN and amounts appear on the contract, title, license and stips, and every copy has to agree before funding.
Each employer and utility prints its own layout, often sent as a phone photo, and lenders set recency rules, such as income dated within 30 days.2
The same Parse call reads a retail installment sale contract, a pay stub and a vehicle title. 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.
Parse reads the packet the dealer uploads, including the contract's disclosure boxes, scanned titles and phone photos of stips, into structured Markdown.
Parse API →Split takes the Parse output and separates the contract, odometer disclosure, title and each pay stub, statement and bill, with the page range of each.
Split API →Extract returns names, addresses, income, VINs and contract terms as JSON that matches the schema for each document type.
Extract API →Ground maps each extracted value to the block, page and bounding box it came from, so a funding analyst checks a name, VIN or income figure on the original page before funding.
Ground docs →Trusted, high-volume processing of the documents involved in auto lending requires handling diverse document types and layouts. A driver's license is very different from a retail installment sale contract, which is different from a bank statement. 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 |
|---|---|---|
| APR, amount financed, finance charge and total of payments | Retail Installment Sale Contract | Contract terms match the approval |
| Number of payments and payment amount | Retail Installment Sale Contract | Term, payment and payment-to-income |
| Cash price, net trade-in and total down payment | Retail Installment Sale Contract | Loan-to-value and deal structure |
| Buyer, VIN, model year and make | Retail Installment Sale Contract | Borrower and collateral match the stips and title |
| Title number, VIN, owner and lienholder | Vehicle Title | Lien recorded in the lender's name |
| VIN, odometer reading and mileage certification | Odometer Disclosure Statement | Mileage matches the contract and title |
| Insurer, named insured and policy period | Auto Policy Declarations Page | Coverage in force at funding |
| Coverage and limit | Auto Policy Declarations Page | Required coverage is on the policy |
| Name, date of birth, issuing state and expiration | Driver's License | Identity matches the application and contract |
| Credit score and total monthly payments | Credit Report | Credit tier and payment-to-income |
| Creditor, account type and payment status | Credit Report | Prior auto credit history |
| Employer, pay date, gross pay and year-to-date gross pay | Pay Stub | Stated income and how recent the stub is |
| Account holder, statement period and total deposits | Bank Statement | Self-employed income and residence |
| Account holder, service address and bill date | Utility Bill | Residence stip |
| Lender, vehicle and principal balance | Auto Loan Statement | Trade-in or refinance payoff |
| Employer and wages | W-2 Form | Annual income |
| Total income and adjusted gross income | Form 1040 | Variable or self-employed income |
Funding leaders measure document work in three places: how many approved deals fund, what each packet costs to review and how many funded loans carry a mismatch.
An approved deal that waits on a kickback can still go to another lender. When contracts and stips are read in minutes, a packet reaches a funding analyst the same day, and fewer deals stall before funding.
Funding analysts read the same name, VIN and amounts on the contract, title and stips. Extracting each value once, with its source attached, lets them check the packet against the approval and open a page only where a value disagrees.
Misstated income and mismatched names or VINs that get past funding become servicing and title problems later. Grounded values let rules and analysts compare each stip with the application and contract before the dealer is paid.
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.
Available on Team and Enterprise plans. VPC and on-prem deployment require an Enterprise plan.
