DataSnipper
DataSnipper automates data extraction from documents into Excel for auditors and accountants. It cross-references figures, verifies source documents, and reduces manual data handling by up to 50%.
Reviewed by Mathijs Bronsdijk · Updated Apr 13, 2026

What is DataSnipper?
DataSnipper is an AI-powered Excel add-in that automates data extraction from documents for financial auditing and accounting workflows. It targets auditors, accountants, and financial analysts who spend hours manually pulling numbers from PDFs, invoices, and bank statements into spreadsheets. Unlike general-purpose OCR or document processing tools, DataSnipper was built specifically for the audit workflow, with cross-referencing and verification baked into the extraction process.
Key Features
- Document Data Extraction: Pull structured data from PDFs, scanned documents, and bank statements directly into Excel cells with AI-powered recognition
- Cross-Referencing: Automatically match extracted figures against source documents to flag discrepancies during audit procedures
- Excel-Native Integration: Runs entirely inside the Excel ribbon, so auditors stay in their existing workflow without switching between applications
- Multi-Document Processing: Handle batches of invoices, confirmations, and supporting documents in a single extraction run
- Audit Trail: Every extracted data point links back to its source document and location, creating a verifiable chain of evidence
- AI-Assisted Verification: Identifies potential mismatches and anomalies in financial data before the auditor reviews them
Use Cases
- External auditors: Extract and verify figures from client-provided financial statements, reducing manual tick-and-tie work by up to 50%
- Financial controllers: Reconcile bank statements and invoices against ledger entries without manual data entry
- Legal and compliance teams: Pull key clauses and figures from contracts for review and case preparation
- Accounting firms: Process large volumes of supporting documentation during year-end audits at scale
Strengths and Weaknesses
Strengths:
- Users consistently praise the interface as intuitive, with minimal training needed for Excel-proficient auditors
- Significant time savings on repetitive extraction tasks, with users reporting 30-50% reduction in manual data handling
- Responsive customer support team that resolves issues quickly
- Reliable performance across different document types and formats
Weaknesses:
- Enterprise pricing is opaque, requiring a sales conversation for larger teams
- Some users report challenges with error handling when documents have unusual formatting
- Integration with systems outside Excel can be complex to set up
Pricing
- Basic: Free tier with limited features and up to 100 documents per month
- Pro: $15/month with advanced features, full integrations, and up to 1,000 documents
- Enterprise: Custom pricing with dedicated support, unlimited documents, and annual contract minimum
A 30-day free trial is available for the Basic tier without a credit card. Student and nonprofit discounts are offered.
FAQ
What is DataSnipper used for?
DataSnipper automates data extraction from documents directly into Excel. Auditors and accountants use it to pull figures from PDFs, bank statements, and invoices, then cross-reference them against financial records without manual data entry.
Does PwC use DataSnipper?
Yes. PwC and Deloitte both use DataSnipper as part of their auditing and data analysis processes. The tool is adopted by several Big Four firms to improve efficiency in handling large datasets.
Is DataSnipper free?
DataSnipper offers a limited free tier for up to 100 documents per month. Full access requires a paid subscription starting at $15/month for the Pro plan. Enterprise pricing is available on request.
Where is DataSnipper in Excel?
After installation, DataSnipper appears as an add-in in the Excel ribbon. Users access all extraction and verification features directly from that toolbar without leaving Excel.
Is DataSnipper considered AI?
DataSnipper uses AI and machine learning for document recognition and data extraction. It goes beyond simple OCR by understanding document structure and context, which improves accuracy when pulling financial data from varied formats.