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Document type

Extracting data from vehicle schedules

Commercial fleet operators and their brokers list every insured unit in a vehicle schedule, usually a spreadsheet in the insured's own columns. Carrier underwriters receive one with each new submission and renewal, a row for every unit. It arrives as an Excel file or PDF export, attached to an email or a public-entity insurance RFP.

Underwriting needs each unit's year, make, model, VIN, body type, stated value and garaging location. A rating system classifies every unit by type and territory and prices physical damage on the stated value. The unit list then carries onto the policy's auto schedule.

Page 3 of a sample Vehicle Schedule, with extracted values outlined
16,756Markdown characters
990parse blocks
758fields extracted
The problem

What makes vehicle schedules hard

Every insured builds its own columns

The value column is headed Agreed Value on one fleet's spreadsheet and Stated Amount or Cost New on another, in whatever position the insured chose.

Units grouped under header rows

A fleet list often groups its units under full-width header rows for each location or department, and a group can continue onto the next page.

Equipment listed among the vehicles

A schedule lists trailers, watercraft and portable equipment alongside road vehicles, so a value column can hold a coverage note where an amount would print.

How it works

Parse, extract, verify

1

Parse

Parse turns the document into reading-order Markdown and a hierarchy of blocks (text, tables, figures), and grounds each block to its exact location on the page.

2

Extract

You define the fields in a JSON schema, and Extract returns their values in a consistent, structured format, each linked back to where it appears in the Markdown.

3

Verify

Each value links back to where it appears, down to the character range in the Markdown, and the page and bounding box in the source document, so every result can be verified.

Worked example

One vehicle schedule, end to end

About this document: Addendum 1 to a property and casualty insurance RFP from the County of Louisa, Virginia. The county published it so bidding insurers could price its fire and EMS program. Its 4 pages open with questions and answers. Pages 3 and 4 carry the vehicle schedule: 86 units in a nine-column table under twelve section headers. Page 3 is featured.

Processing objective: The objective is to capture one fleet line the way an underwriter rates it, from the department header down to the unit's insured value and its garaging station. The department name prints once, in the header row above, and covers every unit beneath it.

Demonstration fields: Department section, Body type, VIN, Agreed value, Station ID

The document

4 of 4 pages
  • Page 1 of the sample Vehicle SchedulePage 1
  • Page 2 of the sample Vehicle SchedulePage 2
  • Page 3 of the sample Vehicle SchedulePage 3shown below
  • Page 4 of the sample Vehicle SchedulePage 4

Where each value came from

Page 3 of the sample Vehicle Schedule, with every featured field outlined
Department sectionlocations[0].section_name
Louisa Vol. Fire Dept.
The region of the sample Vehicle Schedule containing Department section, with the extracted value outlined
extraction_metadata["locations[0].section_name"]
{
  "value": "Louisa Vol. Fire Dept.",
  "page": 3,
  "range": {
    "end": 2103,
    "start": 2081
  },
  "box": {
    "xmin": 0.08308,
    "ymin": 0.09506,
    "xmax": 0.83516,
    "ymax": 0.10679
  }
}
Body typelocations[0].vehicles[0].body_type
Pumper
The region of the sample Vehicle Schedule containing Body type, with the extracted value outlined
extraction_metadata["locations[0].vehicles[0].body_type"]
{
  "value": "Pumper",
  "page": 3,
  "range": {
    "end": 2175,
    "start": 2169
  },
  "box": {
    "xmin": 0.34147,
    "ymin": 0.10678,
    "xmax": 0.48551,
    "ymax": 0.11787
  }
}
VINlocations[0].vehicles[0].vin
4PICV01E5BA011783
The region of the sample Vehicle Schedule containing VIN, with the extracted value outlined
extraction_metadata["locations[0].vehicles[0].vin"]
{
  "value": "4PICV01E5BA011783",
  "page": 3,
  "range": {
    "end": 2201,
    "start": 2184
  },
  "box": {
    "xmin": 0.48551,
    "ymin": 0.10677,
    "xmax": 0.63411,
    "ymax": 0.11783
  }
}
Agreed valuelocations[0].vehicles[0].agreed_value
580,000
The region of the sample Vehicle Schedule containing Agreed value, with the extracted value outlined
extraction_metadata["locations[0].vehicles[0].agreed_value"]
{
  "value": 580000,
  "page": 3,
  "range": {
    "end": 2217,
    "start": 2210
  },
  "box": {
    "xmin": 0.63411,
    "ymin": 0.10677,
    "xmax": 0.73475,
    "ymax": 0.11779
  }
}
Station IDlocations[0].vehicles[0].unique_id
FS1
The region of the sample Vehicle Schedule containing Station ID, with the extracted value outlined
extraction_metadata["locations[0].vehicles[0].unique_id"]
{
  "value": "FS1",
  "page": 3,
  "range": {
    "end": 2229,
    "start": 2226
  },
  "box": {
    "xmin": 0.73474,
    "ymin": 0.10676,
    "xmax": 0.80443,
    "ymax": 0.11776
  }
}

How to try it yourself

Everything on this page is reproducible. The sample, its schema, and the Parse and Extract API outputs are published. You can repeat the same two calls from whichever surface you already build on.

  • Claude Skilldescribe the job; the agent writes it
  • CLIno code at all
  • MCPexpose ADE docs as an agent tool
  • REST APIany language
  • Pythonlandingai-ade
  • TypeScriptlandingai-ade
Why LandingAI?

Documents into data

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The best accuracy on the difficult documents other tools get wrong, such as messy scans, complicated tables, checkboxes and handwriting.

Auditable

Every answer cites its sources. Values come back with an audit trail (page, bounding box, character range) so a human can review them.

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Applies multi-step reasoning, and plans before it acts. Handles documents it has never seen before and adapts to variations.

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Capabilities

Vehicle Schedule processing highlights

No per-insured templates
VINs, year, make and model
Stated and agreed values
Garaging location per unit
Location section headers
Sections across page breaks
Text notes in value columns
Equipment and watercraft rows
XLSX and CSV input
PDF exports and scans
One array row per unit
Bounding box for every value
FAQs

Questions, answered

No. A schema describes each field in words, and can list the alternative headings it goes by. One value field that lists Agreed Value and Stated Amount as alternatives reads each insured's schedule, wherever the column sits.

Yes. Model the schedule as an array of sections, each with its header name and an array of units. Every unit comes back inside the section it was listed under, and a section that continues across a page break returns as one entry.

Pair a numeric value field with a text note field in your schema. A cell holding a coverage note returns in the note with a null value, so the stated values can be totaled across the fleet directly.

Yes. Parse accepts XLSX and CSV files through the API. Spreadsheet content is grounded by cell, so each value carries an address in Excel notation, such as Units!F12, in place of a page and bounding box.

Every extracted value carries the character range it was quoted from in the parsed Markdown. The Ground API maps that range to a page and a bounding box, so a reviewer sees each VIN in the table cell that printed it.

Start extracting vehicle schedule data

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