Corporate resolution register
For Corporate records administration teams
Prepare entity, meeting date and approved actions as written from a resolution with fields you can edit. Authority validation external.
25 free credits/month · No card needed
See the workflow and review checksInspect a document example.
Synthetic documents · Expected output- entity name
- Example Entity - synthetic
- resolution reference
- DEMO-RESOLUTION-0042
- meeting date
- 2026-09-30
- adopted actions
- [ { "action_reference": "DEMO-ACTION-0042", "resolution_text": "Approve the stated action subject to the limit printed in this synthetic source.", "stated_limit_text": "Stated limit text explicitly printed in the original synthetic source for reviewer context." } ]
- signatory names text
- Signatory names text explicitly printed in the original synthetic source for reviewer context.
- stated effective date
- 2026-10-01
{
"entity_name": "Example Entity - synthetic",
"resolution_reference": "DEMO-RESOLUTION-0042",
"meeting_date": "2026-09-30",
"adopted_actions": [
{
"action_reference": "DEMO-ACTION-0042",
"resolution_text": "Approve the stated action subject to the limit printed in this synthetic source.",
"stated_limit_text": "Stated limit text explicitly printed in the original synthetic source for reviewer context."
}
],
"signatory_names_text": "Signatory names text explicitly printed in the original synthetic source for reviewer context.",
"stated_effective_date": "2026-10-01"
}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 resolution arrives for corporate records administration teams. The next step needs entity, meeting date and approved actions as written, recorded under the correct document reference before the information enters your contract document register.
A resolution authorizes an action subject to a stated limit. Preserve that limit for a corporate records reviewer. 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
- Resolution PDFs
- Scanned or photographed resolution 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 entity name, resolution reference, meeting date, your application maps the reviewed record into your contract document register. Keep the original document reference with the result. Authority validation 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 |
|---|---|---|
entity_name | string | Entity name exactly as printed in this resolution. Preserve identifiers and qualifiers; do not look up or infer missing information. |
resolution_reference | string | Resolution reference exactly as printed in this resolution. Preserve identifiers and qualifiers; do not look up or infer missing information. |
meeting_date | date | Meeting date explicitly printed in this resolution. Keep it distinct from other dates; leave it empty if no calendar date is stated. |
adopted_actions | array | Every visible adopted actions row in this resolution. Preserve separate rows, labels and qualifiers; do not merge entries or reconstruct missing ones. |
signatory_names_text | string | Signatory names text exactly as printed in this resolution. Preserve identifiers and qualifiers; do not look up or infer missing information. |
stated_effective_date | date | Stated effective date explicitly printed in this resolution. Keep it distinct from other dates; leave it empty if no calendar date is stated. |
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 resolution authorizes an action subject to a stated limit. Preserve that limit for a corporate records reviewer.
- Check meeting date, stated effective date against their own printed labels; leave an unstated date empty instead of deriving it.
- Compare every adopted actions row with the source, including continuation pages, separate categories and repeated table headings.
Prove the fit on a representative batch.
Test resolution examples from the layouts your team actually receives. Include the exception described here: A resolution authorizes an action subject to a stated limit. Preserve that limit for a corporate records reviewer. Compare the extracted entity name, resolution reference, meeting date 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 resolution 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 corporate records administration teams. Bring difficult resolution 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 corporate resolution register
Which fields can I capture from a resolution?
The starter schema contains entity name, resolution reference, meeting date, adopted actions, signatory names text, stated effective date. 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 corporate resolution register?
A resolution authorizes an action subject to a stated limit. Preserve that limit for a corporate records reviewer. 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 contract document register?
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. Authority validation 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.