Automatic data entry: transfer information from documents, emails, and forms into your systems error-free. Up to 95% time savings.
Manual data entry is one of the most common and simultaneously most unproductive activities in businesses. Employees transfer information from PDF forms into CRM systems, copy data from emails into spreadsheets, type delivery notes into inventory management, and maintain master data in multiple systems simultaneously. Studies show that office workers spend an average of 2.5 hours per day on manual data entry — that's over 30% of productive work time.
The error rate for manual data entry is 1-4% per field. For a form with 20 fields, that statistically means at least one error per document. These errors compound when the same data must be entered into multiple systems: a typo in the customer number causes orders to be misassigned, invoices to go to the wrong address, and customer service to lack a complete overview.
Especially frustrating: in many companies, the needed data already exists digitally — in an email, a PDF attachment, or another system. Yet it's manually retyped because the systems don't communicate with each other. Employees become human interfaces between isolated IT systems — a task that destroys motivation and productivity alike.
The hidden costs of manual data entry extend far beyond time spent: lack of standardization leads to inconsistent datasets that undermine downstream processes like reporting, analytics, and automation. A typo in a postal code can misroute a delivery, a wrong decimal point in a price list can cost thousands. According to IBM, poor data quality costs US businesses $3.1 trillion annually.
Industries with high document volumes are particularly affected: healthcare (patient records), logistics (shipping documents), real estate (lease agreements), and financial services (loan applications). In these sectors, employees often spend 60-70% of their working time entering and verifying data rather than performing value-adding activities.
Our data entry workflow extracts information automatically from any source — PDFs, emails, scanned documents, web forms, API feeds — and transfers it error-free to your target systems. The AI recognizes document types, identifies relevant fields, and validates extracted data against existing master data.
For structured documents (invoices, orders, forms), automatic extraction achieves accuracy of over 98%. Unstructured data from emails or free text is processed by NLP models that reliably recognize contact details, order numbers, product names, and quantities. Every record goes through an automatic quality check: duplicates are detected, formats standardized, and missing required fields flagged.
The workflow synchronizes data in real-time across all connected systems — CRM, ERP, accounting, helpdesk. When a record is updated in one system, the change is automatically propagated to all other systems. This creates a unified data foundation without the typical consistency issues of manual multiple entry.
The automated data entry workflow combines OCR text recognition, AI-powered data extraction, and rule-based validation into an end-to-end solution. Documents are automatically classified (invoice, contract, form, correspondence) and relevant fields extracted — even from handwritten notes and poorly scanned documents.
Integrated plausibility checks validate extracted data against existing databases: customer numbers, postal code mappings, IBAN check digits, and formatting rules are automatically verified. When confidence is low, data points are flagged for human review rather than silently accepting incorrect data. Companies typically report a 90-95% reduction in manual entry effort while simultaneously improving data quality to above 99.5%.
PDFs, scanned documents (JPG, PNG, TIFF), Word files, Excel spreadsheets, email text and attachments, and structured data formats (CSV, XML, JSON). Handwritten documents are processed with specialized handwriting OCR.
The system checks new records against existing entries using configurable fields (e.g., email, phone, customer number). For possible duplicates, the record is flagged for manual review or automatically merged.
Yes, you can define which fields are extracted from which document types, where data is written, and which validation rules apply. The system also learns from your corrections.
We analyze your process and show you the concrete savings potential — no strings attached.
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