Business personal property declaration capture
For Business property tax document teams
Prepare asset categories, acquisition costs and reported year from a property declaration with fields you can edit. Valuation external.
25 free credits/month · No card needed
See the workflow and review checksInspect a document example.
Synthetic documents · Expected output- business name
- Example Harbor Services LLC - synthetic
- jurisdiction
- CA - stated in the synthetic source
- declaration year
- 2026
- property location
- Example property location - synthetic
- asset rows
- [ { "asset_category": "Example asset category - synthetic", "acquisition_year": 2024, "description": "Example document line - original synthetic data", "original_cost": 1250, "stated_basis_text": "Stated basis text explicitly printed in the original synthetic source for reviewer context." } ]
- stated total cost
- 1250
{
"business_name": "Example Harbor Services LLC - synthetic",
"jurisdiction": "CA - stated in the synthetic source",
"declaration_year": 2026,
"property_location": "Example property location - synthetic",
"asset_rows": [
{
"asset_category": "Example asset category - synthetic",
"acquisition_year": 2024,
"description": "Example document line - original synthetic data",
"original_cost": 1250,
"stated_basis_text": "Stated basis text explicitly printed in the original synthetic source for reviewer context."
}
],
"stated_total_cost": 1250
}A complete synthetic document with the visible values shown below. Real responses also include model-reported confidence, validation and result status. Test your own file with this schema.
When this work lands on your desk
A property declaration arrives for business property tax document teams. The next step needs asset categories, acquisition costs and reported year, recorded under the correct document reference before the information enters your preparer intake worksheet.
A declaration lists original acquisition cost rather than current market value. Preserve the labeled basis without valuing the assets. This schema keeps the source labels and values explicit so the reviewer can resolve the issue before downstream use.
Start with the documents you already collect
- Property declaration PDFs
- Scanned or photographed property declaration pages
PDF, JPEG, PNG, TIFF, or WEBP. Send one file per extraction call; create separate schemas when the document types need different fields.
Define the fields
Open the starter schema. Edit field descriptions, mark the values your process requires, and save it in your account.
Extract at intake
Send the document and saved schema slug to
POST /api/v1/extract, or collect the file through a branded upload site.Hand off reviewed data
After checking business name, jurisdiction, declaration year, your application maps the reviewed record into your preparer intake worksheet. Keep the original document reference with the result. Valuation external. Dokyumi processes one file with a saved schema; your application owns record matching, business rules and updates to other systems.
A schema shaped around the next step.
Start with these fields, then adjust them to match your documents and destination system. The example above shows the data object you are asking Dokyumi to extract.
Download editable starter schemaChoose required fields for your process. The starter schema preserves any defined nested fields; refine line-item structures and test them on representative files.
| Field | Type | What to capture |
|---|---|---|
business_name | string | Business name exactly as printed in this property declaration. Preserve identifiers and qualifiers; do not look up or infer missing information. |
jurisdiction | string | Jurisdiction exactly as printed in this property declaration. Preserve identifiers and qualifiers; do not look up or infer missing information. |
declaration_year | number | Declaration year as reported in this property declaration. Preserve the labeled measure and its unit; do not estimate it. |
property_location | string | Property location exactly as printed in this property declaration. Preserve identifiers and qualifiers; do not look up or infer missing information. |
asset_rows | array | Every visible asset rows row in this property declaration. Preserve separate rows, labels and qualifiers; do not merge entries or reconstruct missing ones. |
stated_total_cost | currency | Stated total cost explicitly labeled in this property declaration. Preserve its sign and stated currency; do not calculate a missing amount. |
Give exceptions a clear path to review.
Dokyumi returns a model-reported confidence map, validation details, and a completed or review status for produced results. Check required fields and your business rules before passing data onward.
A completed extraction describes the extraction result. Your application and reviewers own matching, approvals, routing, and updates to other systems.
Read the response contractChecks for this process
- A declaration lists original acquisition cost rather than current market value. Preserve the labeled basis without valuing the assets.
- Check stated total cost for signs, decimal placement and stated currency before any downstream calculation or posting.
- Compare every asset rows row with the source, including continuation pages, separate categories and repeated table headings.
Prove the fit on a representative batch.
Test property declaration examples from the layouts your team actually receives. Include the exception described here: A declaration lists original acquisition cost rather than current market value. Preserve the labeled basis without valuing the assets. Compare the extracted business name, jurisdiction, declaration year and any line rows with a reviewer's checked reference before enabling downstream actions.
Compare extracted values with a reviewer’s reference set. Record corrections, unresolved fields, review time, and credits used before connecting downstream actions.
The free plan’s credit limits apply. Assisted pilots are scoped with Dokyumi before work begins.
Expand when the next process is ready
Add recurring extraction capacity when property declaration intake becomes routine. Reuse this field contract for layouts that express the same information; create a separate schema when the next document has different fields or review requirements.
For a tailored implementation
Discuss document volume, page lengths and schema setup for business property tax document teams. Bring difficult property declaration samples and the review requirements your implementation must preserve.
Talk through document mix and volumeStart small. Add capacity as intake grows.
All self-service plans include the REST API, upload-site webhooks, custom confidence thresholds, and schema inference. Choose by volume, schema count, and upload sites.
Each document uses one credit per 5 pages, rounded up. An 8-page file uses 2 credits. Self-service files can be up to 50 pages; larger files, custom-built schemas, and additional volume are quoted through Enterprise. Full pricing details
Questions about business personal property declaration capture
Which fields can I capture from a property declaration?
The starter schema contains business name, jurisdiction, declaration year, property location, asset rows, stated total cost. Edit those definitions and choose your required fields before saving. Array fields include defined sub-fields so row values have a validation structure.
What needs review in business personal property declaration capture?
A declaration lists original acquisition cost rather than current market value. Preserve the labeled basis without valuing the assets. Check the original source alongside model-reported confidence and validation output; completion status describes the extraction result, not the business decision.
How does this data reach your preparer intake worksheet?
Save the schema, then send one supported PDF or image to POST /api/v1/extract with its slug. The response contains the data, model-reported confidence, validation and result status. Your application maps reviewed values to the destination; upload-site submissions can use a configured webhook. Valuation external.
How do I test this workflow on my own documents?
Start free with 25 credits per month, 2 schemas, and 1 branded upload site. The starter schema opens in the schema builder after sign-up. Edit the fields, save it, and upload a representative file through your account or the extraction API. Account documents are stored according to Dokyumi’s privacy policy; contact hello@dokyumi.com for deletion requests and retention requirements.