Organize self-employed income documents before the analyst takes over
For Mortgage processors reviewing self-employed applicants
Start with a focused Schedule C schema to extract the tax year, business identity and selected printed amounts. Build a reviewable document record before applying your lending policy.
25 free credits/month · No card needed
See the workflow and review checks{
"tax_year": "2025",
"proprietor_name": "Example Applicant — synthetic",
"business_name": "Example Design Studio",
"principal_business": "Graphic design services",
"gross_receipts": 148000,
"total_expenses": 63000,
"net_profit_or_loss": 85000
}Synthetic data, not a live extraction. Real responses also include confidence, validation, and result status.
When this work lands on your desk
A self-employed applicant provides tax return files, and the processor needs to prepare the relevant business schedules for income analysis.
A return packet can contain several businesses, repeated copies and different tax years. Copying amounts without their schedule context makes later comparisons difficult. Extracting one identified schedule per prepared file helps retain that context, while the analyst remains responsible for deciding which schedules and adjustments matter.
Start with the documents you already collect
- Prepared Schedule C PDFs
- Scanned business schedules
- Other return forms through separately defined schemas
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
Your preparation step separates or identifies the intended schedule. Your team matches the extracted business and year, checks the original return and performs any qualifying income calculation outside Dokyumi.
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 schemaStarter fields are optional until you choose which are required. Nested line items need a defined structure if you want validation of their individual values.
| Field | Type | What to capture |
|---|---|---|
tax_year | string | Tax year printed on this schedule. |
proprietor_name | string | Name on this schedule, subject to borrower matching. |
business_name | string | Printed business name when present. |
principal_business | string | The stated principal business or profession. |
gross_receipts | currency | Printed gross receipts or sales for the identified schedule. |
total_expenses | currency | Printed total expenses; retain a negative sign if shown. |
net_profit_or_loss | currency | Printed net profit or loss, with loss represented as negative. |
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
- More than one Schedule C in the submitted file
- Amended and original returns for the same year
- Loss amounts whose parentheses were missed
- Gross receipts confused with net profit
Prove the fit on a representative batch.
Evaluate an authorized sample containing multiple years, a loss-making business and a multi-business packet prepared into separate files. Compare the extracted facts with an analyst’s source checks.
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
A paid plan supports recurring borrower volume and additional schemas for other business return forms. Expand only after validating each form’s field definitions.
For a tailored implementation
Discuss schema setup and custom volume for lending teams whose document requirements span many entity types.
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 self-employed borrower tax intake
Is this a tax or underwriting calculation?
No. The schema extracts visible document facts. Tax interpretation, income adjustments and lending decisions require your qualified reviewers.
Does the API separate an entire return packet?
The extraction endpoint processes one file with your selected schema. Your intake process must prepare files and track expected schedules.
Can I add other business forms?
Yes, define and evaluate a suitable schema for each form. Different forms should not inherit Schedule C field meanings automatically.
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.