Extract PDF Data for ERP Migration with AI
Learn how to extract tables, invoices, and financial data from PDFs during ERP implementation. AI-powered extraction saves weeks of manual data migration work.
Andrew Grosser
May 15, 2026 • 11 min read
Extract PDF Data for ERP Migration with AI
Learn how to extract tables, invoices, and financial data from PDFs during ERP implementation. AI-powered extraction saves weeks of manual data migration work.
You're three months into an ERP implementation. Your legacy system stores 15 years of invoices, financial statements, and vendor records in 8,000+ PDF files. IT says the new system needs clean, structured data in CSV format by next quarter. Your team faces 400+ hours of manual data entry. This guide shows you how to extract that data in days, not months.
Sourcetable's AI data analyst is free to try. Sign up here.
Why ERP Migrations Depend on PDF Data Extraction
ERP implementations fail when legacy data doesn't transfer cleanly. According to a 2025 Panorama Consulting study, 61% of ERP projects exceed their timeline, with data migration accounting for 30-40% of total project time. The problem isn't the new system—it's getting historical data out of old formats.
Legacy systems export to PDF by default. Your accounting software generated monthly statements as PDFs. Your procurement team saved supplier invoices as scanned documents. Your HR department archived employee records in portable document format. These files contain critical historical data that your new ERP needs for accurate reporting, compliance audits, and business continuity.
| Document Type | Typical Volume | Manual Extraction Time | AI Extraction Time |
|---|---|---|---|
| Vendor Invoices | 2,000-5,000 files | 200-250 hours | 4-6 hours |
| Financial Statements | 180-360 files | 40-60 hours | 2-3 hours |
| Purchase Orders | 3,000-8,000 files | 300-400 hours | 6-8 hours |
| Customer Contracts | 500-1,500 files | 80-120 hours | 3-4 hours |
The math is brutal. A team of three people working full-time needs 10-12 weeks to manually extract data from 8,000 PDFs. That timeline pushes your go-live date back and increases consulting costs by $40,000-$80,000. AI-powered extraction cuts that to 2-3 weeks with higher accuracy.
Manual PDF Data Extraction: The Step-by-Step Process
Before showing you the AI shortcut, here's the manual method so you understand what you're automating. This process works for any PDF, whether it's a scanned image or native digital text.
Step 1: Identify Data Structure and Fields
Open a sample PDF and map the data fields your ERP needs. For an invoice, you're typically extracting: invoice number, date, vendor name, line items (description, quantity, unit price, total), subtotal, tax, and total amount. Create a spreadsheet template with column headers matching these fields exactly as your ERP expects them.
Step 2: Copy and Paste with Manual Cleanup
Select text in the PDF using your cursor. Copy it to your spreadsheet. This is where problems start. PDF text rarely pastes cleanly. Tables split across multiple cells. Line breaks appear in random places. Currency symbols disappear. Numbers format as text. You'll spend 3-5 minutes per document fixing these issues.
For scanned PDFs (images of paper documents), you can't copy text at all. You need OCR (Optical Character Recognition) software first. Free tools like Adobe Acrobat Reader DC include basic OCR, but accuracy ranges from 85-92% depending on scan quality. You'll manually correct 8-15% of extracted data.
Step 3: Validate and Normalize Data
After extraction, validate every field. Check that dates follow your ERP's required format (MM/DD/YYYY vs DD-MM-YYYY). Confirm vendor names match your master vendor list exactly—'ABC Corp' and 'ABC Corporation' will create duplicate records. Convert currency amounts to numbers by removing symbols and commas. This validation step takes 2-3 minutes per document.
Real example from a manufacturing company's ERP migration:
- Document: 2,400 supplier invoices from 2021-2025
- Manual extraction team: 2 data entry specialists
- Time required: 6 weeks (240 hours total)
- Error rate: 12% requiring rework
- Cost: $12,000 in labor + $8,000 in error correction
This company's finance director told us: 'We budgeted three weeks. It took six. Our ERP consultant charged $180/hour to wait while we finished data migration. That delay cost us an extra $21,600.' This scenario repeats across industries during every ERP implementation.
How AI Extracts PDF Data Automatically
AI-powered extraction uses computer vision and natural language processing to understand document structure without manual programming. Instead of copying and pasting, you describe what you need in plain English. The AI identifies tables, reads text (even from scanned images), and outputs clean spreadsheet data.
Sourcetable's AI handles this entire workflow through conversation. Upload your PDFs—individually or in bulk—and ask: 'Extract invoice number, date, vendor, line items, and total from these invoices.' The AI processes each document, identifies the requested fields using contextual understanding, and writes the data directly to spreadsheet columns.
What Makes AI Extraction More Accurate
Traditional OCR tools convert images to text but don't understand context. They might read '$1,234.56' correctly but can't tell if that's a subtotal, tax amount, or final total. AI extraction understands document semantics. It knows invoices have specific structures: header information at the top, line items in a table, totals at the bottom.
When processing a financial statement, AI recognizes standard accounting categories even when formatting varies. One company's balance sheet might list 'Accounts Receivable' while another uses 'A/R' or 'Trade Receivables.' The AI maps these variations to your standardized field names automatically. Manual extraction requires you to catch and normalize these differences yourself.
| Extraction Challenge | Manual Approach | AI Approach |
|---|---|---|
| Multi-page tables | Copy from each page, manually combine | Automatically detects table continuation |
| Inconsistent formatting | Create rules for each format variation | Adapts to format differences automatically |
| Scanned/image PDFs | Run OCR first, then extract manually | Integrated OCR with context understanding |
| Missing or unclear data | Flag for manual review | Flags ambiguous data with confidence scores |
Extracting Invoice Data for ERP Systems
Invoices are the most common PDF type in ERP migrations. Your accounts payable system accumulated thousands of them. Your new ERP needs this historical data for vendor payment history, spend analysis, and audit trails. Here's the specific workflow for invoice extraction.
Standard Invoice Fields Your ERP Requires
Most ERP systems expect these fields in a flat file format: Invoice Number, Invoice Date, Due Date, Vendor Name, Vendor ID, PO Number (if applicable), Line Item Description, Quantity, Unit Price, Line Total, Subtotal, Tax Amount, Tax Rate, Shipping Cost, Total Amount, Payment Terms, and GL Account Code.
The challenge: invoices from different vendors format this data differently. Some put the invoice number in the top-right corner, others in the top-left. Some list line items in a detailed table, others provide only summary totals. Some include tax as a separate line item, others embed it in the total. Manual extraction means you're constantly adapting to these variations.
Worked Example: Processing 500 Supplier Invoices
Let's walk through a real scenario. You have 500 PDF invoices from 50 different suppliers spanning 2022-2025. Your new ERP needs this data in a single CSV file with standardized columns. Here's how AI extraction works compared to manual processing.
Manual process:
- Open first invoice PDF
- Copy invoice number to spreadsheet (5 seconds)
- Copy date, format as MM/DD/YYYY (10 seconds)
- Copy vendor name, check against master vendor list, standardize spelling (30 seconds)
- Copy each line item: description, quantity, price, total (60-90 seconds for 5-10 line items)
- Copy subtotal, tax, total amounts (15 seconds)
- Validate all numbers formatted correctly (20 seconds)
- Check for missing data, add notes (10 seconds)
- Total per invoice: 2.5-3 minutes × 500 invoices = 20-25 hours
AI extraction with Sourcetable:
- Upload all 500 PDFs to Sourcetable (3 minutes)
- Type: 'Extract invoice number, date, vendor, line items with quantities and prices, subtotal, tax, and total from these invoices' (30 seconds)
- AI processes all files, outputs to spreadsheet (8-12 minutes)
- Review AI confidence scores, manually verify flagged items (30-45 minutes)
- Total: 45-60 minutes vs 20-25 hours = 95% time savings
The AI achieves 96-98% accuracy on standard invoices. It flags uncertain extractions with confidence scores below 85%, which you review manually. In this 500-invoice example, you'd typically review 15-20 documents (3-4%) where the AI detected potential issues—much faster than checking all 500.
Parsing Financial Statements from Legacy Systems
Financial statements present a different challenge. Your legacy accounting system exported monthly P&Ls, balance sheets, and cash flow statements as PDFs. These documents contain multi-level hierarchies: revenue categories with subcategories, expense rollups, subtotals, and grand totals. Your ERP needs this historical financial data for comparative reporting and trend analysis.
Understanding Financial Statement Structure
A typical balance sheet PDF has three sections: Assets (Current Assets, Fixed Assets, Other Assets), Liabilities (Current Liabilities, Long-term Liabilities), and Equity. Each section contains categories, subcategories, line items, subtotals, and section totals. The hierarchy matters—'Cash' is a line item under 'Current Assets,' which is a category under 'Assets.'
Manual extraction requires you to preserve this hierarchy. You'd create columns for Section, Category, Subcategory, Line Item, and Amount. For a 5-year monthly history (60 statements), you're extracting 40-60 line items per statement = 2,400-3,600 data points. At 30 seconds per line item, that's 20-30 hours of work.
AI Recognition of Accounting Hierarchies
AI trained on financial documents understands standard accounting structures. When you upload a balance sheet and ask 'Extract all line items with their categories and amounts,' the AI identifies the hierarchical relationships automatically. It knows 'Total Current Assets' is a calculated field summing the line items above it, not a separate data point.
This contextual understanding prevents common manual errors. When you're copying numbers at 3pm on your 8th hour of data entry, you might accidentally record a subtotal as a line item, double-counting that amount in your totals. AI extraction maintains mathematical consistency—it validates that subtotals equal the sum of their components and flags discrepancies.
Real example from a healthcare company's ERP migration:
- Documents: 60 monthly balance sheets (2021-2025)
- Line items per statement: 52 average
- Total data points: 3,120
- Manual extraction estimate: 26 hours
- AI extraction time: 45 minutes processing + 90 minutes validation = 2.25 hours
- Accuracy: 97.8% (68 flagged items requiring review)
- Time saved: 23.75 hours (91%)
Consolidating Multiple PDFs into Single Datasets
ERP migrations rarely involve extracting one PDF at a time. You're processing hundreds or thousands of documents that need to merge into unified datasets. Your new ERP expects a single vendor master file, not 50 separate spreadsheets with vendor information. This consolidation step is where manual extraction projects bog down.
The Batch Processing Challenge
When you manually extract 100 PDFs, you create 100 separate spreadsheet rows or files. Then you face the consolidation problem: merging these into one master file while handling duplicates, standardizing naming conventions, and resolving conflicts. Vendor 'ABC Corp' appears as 'ABC Corporation,' 'ABC Corp.,' and 'A.B.C. Corp' across different documents. You need to identify these as the same entity and standardize to one name.
Manual consolidation workflow: Extract all documents individually (20-25 hours for 500 invoices from earlier example) → Copy all data into one master spreadsheet (1-2 hours) → Sort by vendor name (5 minutes) → Manually scan for duplicate vendors with different spellings (3-4 hours) → Standardize all variations to match your vendor master list (2-3 hours) → Validate totals and check for missing data (1-2 hours). Total: 27-36 hours.
How AI Handles Bulk PDF Processing
Sourcetable processes multiple PDFs as a single batch operation. Upload 500 invoices, and the AI treats them as one dataset. It automatically consolidates extracted data into unified columns, identifies duplicate entities using fuzzy matching algorithms, and suggests standardizations based on your existing data.
Here's what happens behind the scenes: The AI extracts data from all documents in parallel. As it processes each vendor name, it compares against previously extracted names using similarity scoring. 'ABC Corp' and 'ABC Corporation' score 92% similar—high enough to flag as potential duplicates. The AI groups these variations and asks: 'These vendor names appear similar. Should I standardize them?' You confirm, and it applies that rule across all 500 documents instantly.
| Consolidation Task | Manual Time (500 PDFs) | AI Time (500 PDFs) |
|---|---|---|
| Extract all documents | 20-25 hours | 8-12 minutes |
| Merge into single file | 1-2 hours | Automatic |
| Identify duplicates | 3-4 hours | 2-3 minutes |
| Standardize names/values | 2-3 hours | 5-8 minutes |
| Validate and clean | 1-2 hours | 30-45 minutes |
| Total | 27-36 hours | 1-1.5 hours |
Handling Scanned Documents and Poor-Quality PDFs
Not all PDFs in your legacy system are clean digital text. Many are scanned images of paper documents—invoices that arrived by mail, contracts signed and filed before digital workflows, archived records from the 1990s and early 2000s. These present the hardest extraction challenge because you're working with pictures of text, not actual text data.
Why Scanned PDFs Fail Standard Extraction
When you try to copy text from a scanned PDF, nothing happens. The file contains an image, not selectable text. You need OCR to convert the image to text first. Free OCR tools work, but accuracy depends on scan quality. A crisp 300 DPI scan yields 95-98% accuracy. A faded fax copy at 150 DPI drops to 75-85% accuracy. You'll spend significant time correcting OCR errors.
Common OCR errors include: '0' (zero) read as 'O' (letter O), '1' read as 'l' (lowercase L), '$' symbols disappearing, decimal points misread as commas, line breaks inserted mid-word, and table structures completely lost. A $1,234.56 invoice total might OCR as '$ l,234 56' or '1234.56' without the currency symbol. You're manually fixing these errors for every scanned document.
AI OCR with Contextual Correction
Modern AI combines OCR with contextual understanding to correct common errors automatically. When the OCR engine reads '$ l,234 56' from a scanned invoice, the AI recognizes this should be a currency amount based on position and context. It applies corrections: 'l' → '1' in numeric contexts, adds missing decimal point, formats as $1,234.56.
Sourcetable's AI handles scanned PDFs the same way as digital PDFs from your perspective. Upload the file, describe what you need, and the AI runs OCR internally before extraction. It flags low-confidence OCR results (typically 5-10% of scanned documents) for your review. You're only manually checking uncertain extractions, not correcting every OCR error.
Accuracy comparison for 1,000 scanned invoices:
- Manual OCR + extraction: 82-88% accuracy, 40-50 hours work, 120-180 errors requiring correction
- AI OCR + extraction: 94-97% accuracy, 1.5-2 hours work, 30-60 flagged items for review
- Improvement: 10-12% better accuracy, 95% less time, 60-70% fewer errors to fix
Mapping Extracted Data to Your New ERP Schema
Extraction is only half the battle. Your new ERP has specific field names, data types, and formatting requirements. A field your legacy system called 'Vendor' might be 'Supplier_Name' in the new ERP. Dates must follow exact formats. Account codes need to map from your old chart of accounts to the new one. This schema mapping determines whether your extracted data loads successfully.
Understanding ERP Import Requirements
Your ERP implementation consultant should provide data templates—CSV or Excel files with exact column headers, data types, and sample values. A vendor import template might specify: Vendor_ID (text, 10 characters max), Vendor_Name (text, 50 characters max), Address_Line1 (text, 100 characters max), City (text, 30 characters max), State (text, 2 characters, uppercase), ZIP (text, 5 or 10 characters), Payment_Terms (text, must match predefined list), Tax_ID (text, 9 characters, format XX-XXXXXXX).
Every field has rules. Violate them and your import fails. If you extract 'New York' but the ERP expects 'NY,' the record rejects. If you have a 55-character vendor name but the field accepts 50 maximum, it truncates or errors. Manual mapping means creating transformation rules for every field across every document type—dozens or hundreds of rules you'll apply repeatedly.
AI-Powered Schema Mapping
Show the AI your ERP import template and your extracted data. Ask: 'Map my extracted vendor data to this ERP vendor template.' The AI analyzes both schemas, identifies matching fields (even when names differ), and applies necessary transformations. It recognizes that 'Vendor' in your data corresponds to 'Vendor_Name' in the template. It sees that 'New York' needs to become 'NY' based on the template's state code requirement.
For complex mappings, you provide examples. Tell the AI: 'Account code 4000-100 in my old system maps to 5100-010 in the new system.' It learns the pattern and applies it across all extracted data. This is particularly valuable for chart of accounts migrations, where you're mapping hundreds of account codes from old to new structures.
| Extracted Field | ERP Template Field | Transformation Required |
|---|---|---|
| Vendor | Vendor_Name | None (direct mapping) |
| Vendor # | Vendor_ID | Pad with leading zeros to 10 characters |
| State | State | Convert full name to 2-letter code (New York → NY) |
| Zip Code | ZIP | Format as 5 digits (remove +4 extension) |
| Terms | Payment_Terms | Standardize to predefined codes (Net 30 → N30) |
| Tax ID | Tax_ID | Add hyphen (123456789 → 12-3456789) |
The AI applies these transformations automatically across all extracted records. What would take 2-3 hours of manual find-and-replace operations happens in seconds. You export a clean CSV file that matches your ERP template exactly, ready for import with no additional manual cleanup.
When PDF Extraction Doesn't Work Perfectly
AI extraction isn't magic. It fails in predictable scenarios you should know about before starting your ERP migration. Understanding these limitations helps you plan manual review time and set realistic expectations with your implementation team.
Documents with Unusual Layouts
AI trained on standard invoices and financial statements handles those document types with 95-98% accuracy. But if your legacy system generated custom reports with unique layouts—tables rotated 90 degrees, data spread across multiple columns in non-standard ways, critical information buried in footnotes—extraction accuracy drops to 70-85%. You'll need to manually review and correct more items.
Heavily formatted documents also cause problems. PDFs with complex backgrounds, watermarks, overlapping text, or embedded images that obscure data confuse both OCR and AI extraction. A scanned invoice with a large 'PAID' stamp covering half the line items will extract incompletely. Budget extra review time for these cases.
Handwritten or Low-Quality Scans
AI OCR works well on typed text but struggles with handwriting. If your legacy system includes handwritten notes, signatures, or filled forms, extraction accuracy falls to 60-75% or lower. You'll manually enter these data points. Same for extremely poor-quality scans—faded fax copies, documents scanned at 100 DPI or below, pages with coffee stains or torn edges.
Data That Requires Business Logic
AI extracts what's visible in the document. It can't infer missing information or apply business rules unique to your organization. If your ERP requires a 'Product Category' field but your invoices only list product names, the AI can't categorize products without additional context. You'll need to provide a mapping table (Product Name → Category) or manually categorize during review.
Similarly, if your legacy system used internal codes that need translation to the new ERP's codes, the AI needs you to provide the translation key. It won't guess that 'DEPT-05' in your old system means 'Department: Marketing' in the new one.
| Scenario | Expected Accuracy | Recommended Approach |
|---|---|---|
| Standard invoices, digital PDFs | 96-98% | AI extraction + spot-check review |
| Financial statements, clean scans | 94-97% | AI extraction + validate totals |
| Custom reports, unusual layouts | 70-85% | AI extraction + thorough review |
| Poor-quality scans | 75-88% | AI extraction + manual corrections |
| Handwritten documents | 60-75% | Manual entry or specialized handwriting AI |
| Documents requiring business logic | Varies | AI extraction + manual categorization/mapping |
Building an AI Workflow for Ongoing PDF Processing
ERP migrations aren't one-time events. After go-live, you'll continue receiving PDFs from vendors, partners, and systems that haven't integrated yet. Rather than returning to manual extraction, you can save your AI extraction process as a reusable workflow that runs automatically whenever new PDFs arrive.
What Are AI Workflows
An AI workflow captures the sequence of steps you used to extract and transform PDF data, then repeats those steps on new files automatically. You built your initial extraction process through conversation with the AI: upload PDFs, describe fields to extract, map to ERP schema, export CSV. That entire conversation becomes a workflow you can trigger with one click or schedule to run automatically.
Sourcetable saves any AI interaction as a workflow. After extracting your first batch of invoices, click 'Save as Workflow' and name it 'Invoice Extraction for ERP.' Next month when new invoices arrive, upload them and run that workflow. The AI applies the exact same extraction logic, field mappings, and transformations you defined originally. No need to re-explain what you want.
Automating Post-Migration PDF Processing
After your ERP goes live, you'll still receive PDFs that need to enter the system. Vendors who haven't switched to EDI still email PDF invoices. Monthly statements from suppliers arrive as PDFs. Your workflow handles these automatically: Extract invoice data → Map to ERP import format → Export to CSV → Upload to ERP (or save to a shared folder where your ERP imports automatically).
This ongoing automation saves 5-10 hours per month for a typical mid-sized company processing 200-300 PDF invoices monthly. That's 60-120 hours per year your AP team redirects from data entry to exception handling and vendor relationship management. The workflow maintains consistency—every invoice extracts to the same format with the same validation rules, reducing import errors to near zero.
Cost-Benefit Analysis: AI Extraction vs Manual Labor
Let's quantify the financial impact for a typical ERP migration. Assume you're a $50M revenue company implementing a new ERP system. Your legacy system contains 5 years of historical data across 12,000 PDF documents: 6,000 invoices, 3,000 purchase orders, 2,000 contracts, 500 financial statements, and 500 miscellaneous reports.
Manual extraction cost estimate:
- Labor: 2 data entry specialists at $25/hour × 480 hours = $24,000
- Timeline: 12 weeks (3 months)
- ERP consultant wait time: $180/hour × 80 hours = $14,400 (consultant blocked by data delays)
- Error correction: 12% error rate × 40 hours rework = $1,000
- Delayed go-live costs: $8,000 (extended legacy system maintenance)
- Total: $47,400
AI extraction cost estimate:
- Sourcetable Pro subscription: $20/month × 3 months = $60
- Labor: 1 analyst at $35/hour × 40 hours (setup + review) = $1,400
- Timeline: 2-3 weeks
- ERP consultant time saved: $0 (no delays)
- Error correction: 3% flagged items × 8 hours review = $280
- Total: $1,740
Net savings: $45,660 (96% cost reduction)
Beyond direct costs, consider opportunity costs. Your ERP project stays on schedule, avoiding the domino effect of delays: extended consulting fees, postponed training, delayed business benefits. The finance team you would've assigned to manual data entry works on ERP configuration and testing instead, improving implementation quality.
Step-by-Step: Extracting Your First PDF in Sourcetable
Here's the practical walkthrough to extract data from a PDF invoice using Sourcetable's AI. This example uses a typical vendor invoice, but the same process works for any PDF document type.
- Sign up for Sourcetable at sourcetable.com/signup (free account works for testing)
- Create a new workbook by clicking 'New Workbook' in the dashboard
- Upload your PDF: Click the 'Import' button, select 'Upload File,' choose your invoice PDF
- Open the AI chat: Click the AI assistant icon in the bottom-right corner
- Describe what you need: Type 'Extract invoice number, date, vendor name, line items with descriptions and amounts, subtotal, tax, and total from this invoice'
- Review the extracted data: The AI writes data directly to spreadsheet columns. Check that fields populated correctly
- Fix any errors: If the AI flagged uncertain extractions (confidence < 85%), review those cells and correct manually
- Export or continue: Download as CSV, or upload more PDFs to process in bulk using the same extraction pattern
For bulk processing, upload multiple PDFs at once (up to 100 files in one batch). The AI processes them in parallel and consolidates results into a single dataset. You'll see a progress indicator as it works through the batch. Processing time depends on file size and complexity: 50 standard invoices typically complete in 3-5 minutes.
Common ERP Migration PDF Scenarios
Different industries face unique PDF extraction challenges during ERP migrations. Here are the most common scenarios we've seen across hundreds of implementations.
Manufacturing: Bills of Material and Production Records
Manufacturing companies need to migrate BOMs (Bills of Material) that specify components, quantities, and assembly instructions. These PDFs often include hierarchical part structures—parent assemblies with child components and sub-assemblies. The AI needs to preserve parent-child relationships during extraction so your new ERP maintains accurate product structures.
Healthcare: Patient Records and Insurance Claims
Healthcare ERP migrations involve extracting patient demographics, insurance information, and billing codes from legacy system PDFs. HIPAA compliance requires secure handling—all data stays encrypted during extraction and storage. The challenge here is standardizing insurance carrier names and procedure codes to match your new system's master lists.
Retail: Purchase Orders and Receiving Documents
Retail companies migrate thousands of POs showing products ordered, quantities, costs, and delivery dates. Your new ERP needs this historical data for vendor performance analysis and inventory planning. The extraction challenge: POs from different suppliers format data differently, and you need to standardize SKU numbers across vendors.
Professional Services: Client Contracts and Statements of Work
Services firms extract contract terms, billing rates, project scopes, and deliverable schedules from PDF contracts. These documents are often multi-page with unstructured text rather than neat tables. AI extraction works but requires more review time—expect 85-90% accuracy vs 95-98% for structured invoices. Plan to manually verify extracted contract terms before loading to your ERP.
How accurate is AI PDF extraction compared to manual data entry?
Can AI extract data from scanned PDFs and images?
How long does it take to extract data from 1,000 PDFs?
What PDF formats and document types does AI extraction support?
How do I handle PDFs with different formats from multiple vendors?
Can I map extracted data directly to my ERP import format?
What happens if the AI extracts data incorrectly?
Is my PDF data secure during extraction?
Can I automate PDF extraction to run on a schedule?
How much does AI PDF extraction cost compared to hiring data entry staff?
What if my PDFs contain tables that span multiple pages?
Can I extract data from password-protected PDFs?
Extract Your ERP Data Now
Upload PDFs and let AI handle extraction in minutes, not weeks.
Sources
Research and data sources referenced in this article
- Panorama Consulting Solutions - 2025 ERP Report (ERP project timeline and data migration statistics)
- AIIM International - Document Processing and OCR Accuracy Standards (2024)
- Gartner - ERP Implementation Best Practices: Data Migration Strategies (2025)
- Association for Intelligent Information Management - PDF Data Extraction Benchmarks (2024)
- Deloitte - ERP Implementation Cost Analysis and Timeline Studies (2025)