Document-to-system entry pairs for back-office data entry agents

Related pages cover extraction labels taken from posted records, structured-output fine-tuning data and the agent and workflow data hub; SourceX's document understanding training data page covers reading alone.

What a posted record adds to the document

A posted entry holds four kinds of value: copied from the page, matched to master records, decided by the clerk under local policy, or filled in by the system. Extraction covers only the first; an entry agent needs all four, so the dataset must label each field's provenance.

One data entry agent vendor describes this pipeline (market practice, not a standard): intake by email, upload or scan; OCR and field extraction; business-rule validation; human review of exceptions; and entry through APIs into systems such as Salesforce, NetSuite and SAP, or UI automation where no API exists [1]. Pairs need evidence from each stage, not just the PDF and final record. A USPTO patent on document extraction notes that even correctly read characters can land in the wrong field when the system lacks context [2].

Where entry pairs come from, and the key that links them

Pairs exist wherever staff re-key documents into a system of record, but can be rebuilt reliably only where the record points back to the source: an attachment or archive ID, a scan batch number, an email message ID, or a reference field filled from the document.

One vendor describes producer-services staff re-keying applications and commission statements into agency management systems [3]; another converts faxed or emailed ACORD forms into ACORD XML for carrier underwriting systems [4]. Oracle's FLEXCUBE banking documentation has back-office clerks input contracts that managers authorize [5], a maker-checker review inside each pair.

Ask how each pair was linked: matching on vendor name, date and amount can create false pairs, so require the share linked by a stored ID and a hand-checked sample (record linkage quality for multi-system datasets). Allow one-to-many links both ways, since one scanned PDF can hold several invoices and one invoice can be split across postings.

Illustrative pair: one invoice, three record versions

A complete pair ties the original file to a dated context snapshot and to every version of the record, with provenance and page evidence per field.

Illustrative example: invented to show structure; it does not describe an available dataset.

{
  "pair_id": "dse-ap-000731",
  "workflow": "ap_invoice_entry_non_po",
  "document": {"file": "docs/dse-ap-000731.pdf", "sha256": "4b1e...9c07", "pages": 1,
               "channel": "ap_mailbox", "received_at": "2026-02-03T14:12:00Z", "intake_id": "scan-batch-0219/07"},
  "context_snapshot": {"as_of": "2026-02-03",
                       "vendor_master": "snap/vendors_2026-02-03.parquet",
                       "open_po_lines": "snap/open_po_lines_2026-02-03.parquet",
                       "chart_of_accounts": "coa-2026.1", "approval_limits": "ap-policy-v7"},
  "target": {"system": "erp", "object": "supplier_invoice", "entry_path": "manual_ui"},
  "link": {"method": "attachment_id_on_record", "documents_per_record": 1},
  "first_entry": {"at": "2026-02-04T09:31:00Z", "actor": "human:ap_clerk_14", "status": "parked",
    "fields": {
      "vendor_id":     {"value": "V-PS-3381", "provenance": "looked_up"},
      "invoice_ref":   {"value": "INV-20817", "provenance": "copied", "evidence": {"page": 1, "text": "Invoice No. INV-20817"}},
      "invoice_date":  {"value": "2026-01-29", "provenance": "copied", "evidence": {"page": 1, "text": "29 Jan 2026"}},
      "gross_amount":  {"value": "2140.00", "provenance": "copied", "evidence": {"page": 1, "text": "Total due 2,140.00"}},
      "po_line":       {"value": null, "provenance": "looked_up", "note": "no_open_po_found"},
      "gl_account":    {"value": "640100", "provenance": "decided"},
      "cost_center":   {"value": "CC-4410", "provenance": "decided"},
      "tax_code":      {"value": "U1", "provenance": "decided"},
      "payment_terms": {"value": "NET30", "provenance": "defaulted"}
    }},
  "posted": {"at": "2026-02-05T16:02:00Z", "actor": "human:ap_supervisor_03",
             "changed_at_review": {"tax_code": ["U1", "I1"]}},
  "corrections": [{"at": "2026-02-19T10:47:00Z", "actor": "human:ap_clerk_09", "type": "reversal_and_repost",
                   "reason_code": "wrong_cost_center", "changed": {"cost_center": ["CC-4410", "CC-4470"]}}],
  "label_policy": {"gold": "settled_state", "settle_rule": "after_period_close"},
  "disposition": "entered",
  "privacy": {"bank_details": "excluded_flag_only", "tax_ids": "excluded", "people": "role_pseudonyms",
              "names_on_document": "surrogates_matching_in_image_text_and_record"}
}

Three versions let you pick the label: the first entry (a human baseline), the reviewed posting, and the settled state. Without the dated snapshot, a replayed agent is graded against since-closed cost centers or matches vendors created later.

Corrections, rejections and documents nobody entered

The most useful labels sit outside the clean posted record: corrections after posting show where entry went wrong, and documents that were never entered show when an agent should not act.

• Post-posting corrections. Reversal and re-post, a credit memo against a duplicate payment, a reclassification entry, or a field edit in change history. Ask for reason codes and links to the original pair, and fix a settle rule, such as after period close (field-level audit trails).
• Non-entries. One accounting product tracks documents as open, data-entry completed, in process or completed, and lets staff flag unreadable scans or duplicates as unprocessable [6]. Request dispositions with counts (duplicate, statement rather than invoice, wrong addressee, unreadable, forwarded); without them an agent never learns when to reject, hold or escalate (exception handling records).
• Review-queue edits. In one vendor's human-in-the-loop design, any field below a confidence threshold such as 0.90 sends the document to human review [7]. Such a log pairs machine proposals with human final values, but only for doubted fields (human override and correction logs).
• Bot and integration writes. RPA bots and import jobs post records too, so tag actor type on every version (RPA bot logs as agent data).

Training and scoring extraction-to-action agents

Score an entry agent against the settled record, one provenance class at a time, and score disposition separately; one exact-match number hides an agent that reads well but codes badly.

• Copied fields: accuracy after normalizing dates, amounts and identifiers.
• Lookups: the right vendor, customer or PO line, or a correct "no match".
• Decisions: GL account, cost center, tax code and routing, benchmarked against the clerk's first-entry error rate.
• Disposition and overwrites: enter, park, hold, reject or escalate; unsupported changes to defaulted fields.

For replay, load the snapshot into a sandbox and compare its end state with the settled record. τ-bench scores agents by comparing the final database state with an annotated goal state, and adds pass^k, the probability of success in all k repeated trials of a task [8]; see seed data for enterprise agent sandboxes.

Public document datasets test only the reading step. FUNSD annotates 199 noisy scanned forms for entity labeling and linking [9]; RealKIE offers five enterprise extraction datasets, including FCC invoices, SEC S-1 filings and non-disclosure agreements [10]; and a 2025 study annotated 102 invoices following the key-information and line-item tasks of the DocILE benchmark [11]. None records what a business entered, matched or coded. Because coding reflects one company's chart of accounts, source pairs from several companies or pass the policy as input so the agent does not memorize account numbers.

Bank details, tax IDs and counterparty documents

Entry pairs carry other parties' data: vendor bank accounts, taxpayer IDs, remit-to addresses and negotiated prices, plus consumer details on insurance and lending forms. Exclude bank and tax identifiers by default but flag that they were present or changed, since a bank-detail change near a payment is a control point.

• Surrogates, not masks. If the agent must handle these fields, use format-preserving surrogates applied identically to page image, OCR text and record (masking versus surrogate replacement).
• Counterparty documents. Invoices and price lists may fall under confidentiality terms in supply contracts, so the license must cover documents received from third parties (license terms for agent workflow data).
• Financial institutions. Under Regulation P, the CFPB's Gramm-Leach-Bliley Act privacy rule, a recipient of nonpublic personal information from a nonaffiliated financial institution faces limits on reuse and redisclosure [12].
• California consumers. Under the CCPA, deidentified status requires, among other conditions, that the holding business contractually bind recipients to the definition's requirements [13] (CCPA deidentified data obligations).

For datasets SourceX sources, rights review checks that the business owns or may share the records and that required consents are in place. Names, emails, phone numbers and account numbers are removed or replaced before delivery, with the method recorded and a sample checked; no de-identification method is perfect. Health claim forms need HIPAA de-identification (Safe Harbor or Expert Determination) before SourceX considers them for a license.

This page is general information, not legal advice. Confirm requirements with counsel for your jurisdiction and use case.

Request template for document-to-entry pairs

Name the workflows, target objects, record versions and dispositions you need so a supplier can check its systems before sampling.

On a sample, check file hashes, that no snapshot postdates its document, that corrections link to their pairs, and that pseudonyms join across tables (evaluating an agent data sample; writing an agent data specification).

SourceX sources operational datasets, including documents and finance workflows, from US companies on request rather than from stock, so a request does not guarantee a match. To scope a back-office automation dataset, send SourceX your document-to-entry specification. Its pages on licensing invoices and receipts and scanned forms and handwritten documents cover the documents alone.

Building agents that need real document-entry histories?

Describe the workflows, document types, target systems and record versions your agent needs. SourceX looks for US businesses that hold matching records, checks the data and the supplier's licensing permissions, and manages the license, delivery and payment; nothing is contracted until a supplier agrees. Specify your workflow dataset.