Orders, delivery notes and forms arrive by email and someone types them into the system — then into a second one.
Invoices, statements and remittances keyed by hand, with the errors found at month end.
Customer POs, bills of lading and packing lists in every format a customer can invent.
Insurance agencies, law firms, medical practices and real estate offices with hundreds of documents a week.
Numbers from a 2025 survey of 500 US professionals and industry error-rate studies — manual entry is slow, and the errors cost more than the typing.
a year per employee is what manual data entry costs US companies, by a survey of 500 professionals.
Parseur survey, 2025 ↗a week go to moving data by hand; for people in IT and finance it is 20+ hours.
Parseur survey, 2025 ↗of fields are keyed wrongly even by experienced operators; in complex processes under load it rises to 18–40%.
DigiParser ↗is the cost of one entry error once investigation, correction, refunds and the conversation with the customer or vendor are counted.
DigiParser ↗of respondents feel burned out or frustrated by repetitive data tasks.
Parseur survey, 2025 ↗have never used automation for data extraction — and a quarter of them did not know such tools exist.
Parseur survey, 2025 ↗Put your monthly volume into the calculator and see hours and dollars.
Data entry automation replaces the manual path a document takes — open the email or scan, find the fields, type them into the ERP or CRM, copy them into a second system, check for typos and duplicates, file the original — with a workflow that reads the document once and does the rest on its own. In most offices the time is not spent on decisions but on moving the same values between an inbox, a spreadsheet and two business systems. That is exactly the part automated data entry removes.
We build it around the tools you already use rather than a new platform. A PDF, scan, photo or web form arriving in the shared inbox or a cloud folder is read by OCR and a language model into a strict schema, validated by code, matched to your catalogue and master data and checked for duplicates. Clean records are written to QuickBooks, NetSuite, Salesforce, HubSpot, Airtable or Google Sheets — and to a legacy system by import file where there is no API. Anything uncertain, a handwritten quantity or a changed bank account, stops and goes to a person with the field highlighted.
Documents are in the system within minutes instead of days, the same value is never typed twice, and errors are caught by rules before they reach the books rather than by a customer afterwards. Your team reviews only the documents that raise a real question, and the weekly digest shows the accuracy per document type, so automating data entry is something you can measure, not something you have to trust.
The run path of one document. Each PDF, scan or form travels it on its own; anything the model is not sure about stops and goes to a person with the field highlighted.
Watches the shared inbox, a web-form webhook (Typeform, Jotform, Gravity Forms) and a scan folder in the cloud; every file starts its own run.
A language model decides whether this is an order, an invoice, a delivery note, an application or something new, and picks the schema.
OCR (Google Document AI, AWS Textract or Azure Document Intelligence) plus the model read the fields into strict JSON — on any layout.
Dates, amounts, tax IDs and reference numbers must be well-formed and line items must add up; otherwise the run stops here.
Maps product codes, customers and vendors to your catalogue and master data, with fuzzy matching where a customer writes the name differently.
The same reference, amount and date must not already exist in any target system. Duplicates are closed with a note, not written twice.
A new bank account, a new price or a new address is never applied silently — it is routed to the owner of that data for approval.
Low-confidence fields, handwriting and damaged scans go to the review queue instead of into your systems.
Opens a review card with the document next to the extracted fields; one click confirms or corrects, and the correction is learned.
Creates or updates the record in the ERP, the CRM and the sheet one after another, with the IDs cross-referenced.
For a system without an API, builds the import file in its exact format or drives its screens with an RPA step.
Saves the original under a stable name with type, date, counterparty and reference in Google Drive, SharePoint or Dropbox.
Friday summary in Slack: documents processed, fields corrected by people, duplicates caught and the current accuracy per document type.
Typical minutes per document for an operations or accounting clerk. Your own numbers go into the calculator below.
Based on 11 min by hand and 36 sec with the flow per item, from the table above.
The flow reads, validates, matches and writes. People keep every decision about what the data means.
When the model is not sure, a person confirms the field with the document right next to it.
They are routed to the review queue rather than guessed at.
A total that does not match the order or an unknown customer is a decision, not a data-entry task.
New bank details, prices and addresses are approved by the owner of that data.
A periodic sample of processed documents is reviewed by your team and the accuracy is reported.
A new layout gets a schema and a test run before it is processed unattended.
Flip a switch to hand a step to the flow or take it back.
Benchmarks from the Parseur 2025 survey and DigiParser’s error-rate review: the goal is a document that is read once, checked by rules and touched by a person only when there is a real question.
We map the process as it runs today, count the minutes and agree what the flow must never do on its own.
A working flow on your real data, in a sandbox. You see every run and every exception.
Edge cases, approvals and alerts, then the switch-over — with the old way kept as a fallback.
Monitoring, fixes when a vendor changes a format, and a monthly report of hours saved.
Printed PDFs and clean scans read reliably; the real number is measured on your own sample before launch and reported every week after. Every field is also checked by rules — totals, formats, reference data — so a misread is caught before it is written, not at month end.
Scans go through the same OCR and language-model step as PDFs. Handwriting is read where it is legible and routed to a person with the field highlighted where it is not — it is never guessed.
It lands in a review queue in Airtable, Retool or a Slack form with the original next to the extracted fields. One click confirms or corrects, the record is written, and the correction improves the next run.
Yes. The flow builds the import file in the exact format the system expects, or drives its screens with an RPA step, while everything else — reading, validation, archiving — stays the same.
Put your own volume into the calculator above. Teams handling a few hundred documents a month usually win back days of work every month; at very low volumes a simple parser subscription may be enough.
On your own server or in your own cloud account. The OCR and language-model steps can run locally so documents never leave your network, and every run is logged.
Rules do the checking, not the model: a field is written only when formats, totals and master data agree, and anything uncertain goes to a person. Accuracy per document type is reported weekly, so you see the number rather than trust it.
Describe it in two sentences — we reply within a day with a workflow sketch.
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