Loan approval letter condition capture
For Funding operations condition coordinators
Prepare amount, expiry and stated conditions from an approval letter with fields you can edit. No approval issuance.
25 free credits/month · No card needed
See the workflow and review checksInspect a document example.
Synthetic documents · Expected output- applicant name
- Example Applicant - synthetic
- lender name
- Example Lender - synthetic
- approval reference
- DEMO-APPROVAL-0042
- approved amount
- 1250
- approval expiry
- 2026-11-30
- condition rows
- [ { "condition_reference": "DEMO-CONDITION-0042", "condition_text": "Subject to the stated document review before the next step.", "stated_status": "Submitted for review", "stated_due_date": "2026-11-30" } ]
{
"applicant_name": "Example Applicant - synthetic",
"lender_name": "Example Lender - synthetic",
"approval_reference": "DEMO-APPROVAL-0042",
"approved_amount": 1250,
"approval_expiry": "2026-11-30",
"condition_rows": [
{
"condition_reference": "DEMO-CONDITION-0042",
"condition_text": "Subject to the stated document review before the next step.",
"stated_status": "Submitted for review",
"stated_due_date": "2026-11-30"
}
]
}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
An approval letter arrives for funding operations condition coordinators. The next step needs amount, expiry and stated conditions, recorded under the correct document reference before the information enters your loan intake worksheet.
A letter describes conditions that must be satisfied before funding. Extract their exact wording without treating an approval letter as evidence of completed funding. 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
- Approval letter PDFs
- Scanned or photographed approval letter 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 applicant name, lender name, approval reference, your application maps the reviewed record into your loan intake worksheet. Keep the original document reference with the result. No approval issuance. 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 |
|---|---|---|
applicant_name | string | Applicant name exactly as printed in this approval letter. Preserve identifiers and qualifiers; do not look up or infer missing information. |
lender_name | string | Lender name exactly as printed in this approval letter. Preserve identifiers and qualifiers; do not look up or infer missing information. |
approval_reference | string | Approval reference exactly as printed in this approval letter. Preserve identifiers and qualifiers; do not look up or infer missing information. |
approved_amount | currency | Approved amount explicitly labeled in this approval letter. Preserve its sign and stated currency; do not calculate a missing amount. |
approval_expiry | date | Approval expiry explicitly printed in this approval letter. Keep it distinct from other dates; leave it empty if no calendar date is stated. |
condition_rows | array | Every visible condition rows row in this approval letter. Preserve separate rows, labels and qualifiers; do not merge entries or reconstruct missing ones. |
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 letter describes conditions that must be satisfied before funding. Extract their exact wording without treating an approval letter as evidence of completed funding.
- Check approval expiry against their own printed labels; leave an unstated date empty instead of deriving it.
- Check approved amount for signs, decimal placement and stated currency before any downstream calculation or posting.
- Compare every condition rows row with the source, including continuation pages, separate categories and repeated table headings.
Prove the fit on a representative batch.
Test approval letter examples from the layouts your team actually receives. Include the exception described here: A letter describes conditions that must be satisfied before funding. Extract their exact wording without treating an approval letter as evidence of completed funding. Compare the extracted applicant name, lender name, approval reference 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 approval letter 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 funding operations condition coordinators. Bring difficult approval letter 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 loan approval letter condition capture
Which fields can I capture from an approval letter?
The starter schema contains applicant name, lender name, approval reference, approved amount, approval expiry, condition rows. 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 loan approval letter condition capture?
A letter describes conditions that must be satisfied before funding. Extract their exact wording without treating an approval letter as evidence of completed funding. 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 loan 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. No approval issuance.
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.