Setting up AI document processing for your business is simpler than most teams expect. The technology has matured to the point where you can go from sign-up to processing real documents in under an hour — if you approach it methodically.
This guide walks through the practical steps.
Step 1: Audit your document types
Before choosing a tool or configuring anything, understand what you are working with.
List every document type your team processes regularly:
- Invoices from vendors
- Receipts and expense documentation
- Contracts and agreements
- Purchase orders
- Bank and financial statements
- Medical or patient forms
- Tax documents
- Shipping and logistics documents
For each type, note:
- Volume: How many per week/month?
- Format: PDF, scanned paper, phone photos, email attachments?
- Quality: Clean typed text? Handwritten? Faded or damaged?
- Data needed: What specific information do you need to extract?
- Downstream system: Where does the extracted data go?
This audit tells you which document type to start with (highest volume + clearest data need) and what processing capabilities you require.
Step 2: Start with one document type
Do not try to set up everything at once. Pick your highest-volume or highest-pain document type and get it working well before expanding.
For most businesses, this is invoices — they are high volume, have clear data extraction needs (vendor, amount, date, line items), and the ROI is easy to measure.
Step 3: Configure your first Smart Flow
In PaperAI, create a Smart Flow for your chosen document type.
Choose your AI model
- Standard models (2-5 credits/page): Use for clean, typed documents. Fast and cost-effective.
- Premium models (8-10 credits/page): Use for handwriting, faded scans, or complex layouts.
Start with a standard model. If accuracy is lower than expected, switch to premium.
Define extraction fields
Specify what data you need from each document. For invoices:
| Field | Type | Example | |---|---|---| | Vendor Name | text | Acme Office Supplies | | Invoice Number | text | INV-2026-0423 | | Invoice Date | date | 2026-03-15 | | Due Date | date | 2026-04-14 | | Line Items | array | (desc, qty, unit_price, total per item) | | Subtotal | currency | $1,250.00 | | Tax | currency | $100.00 | | Total | currency | $1,350.00 |
Set accuracy threshold
For Business plans and above, you can set a confidence threshold for auto-approval:
- 95%+: Conservative. Most documents go to review. Good for starting out.
- 90%: Moderate. Routine documents auto-approve. Good for established workflows.
- 85%: Aggressive. Only problematic documents get flagged. Use after you trust the system.
Start conservative and lower the threshold as you gain confidence in the output quality.
Step 4: Test with real documents
Upload 20-30 real documents and process them through your Flow. Review every result in the side-by-side view:
- Are the extracted fields correct?
- Is the document structure preserved?
- Are tables accurately captured?
- Does the accuracy score reflect the actual quality?
Note any patterns in errors. If certain fields are consistently wrong, adjust your extraction configuration. If document quality is the issue, switch to a premium model.
Step 5: Train your review team
If multiple people will review AI output, establish clear guidelines:
- What to check: Focus on extracted data fields, not full-text accuracy. The data is what enters your systems.
- When to reject: If key fields are wrong and cannot be easily corrected in the editor.
- When to re-convert: If accuracy is low, try a different AI model before manually correcting.
- How to approve: Verify key fields, approve, and the document moves to the export queue.
Step 6: Scale up
Once your first document type is running smoothly:
- Increase volume — process all documents of that type through PaperAI
- Add a second document type — create a new Smart Flow
- Add team members — assign roles (Owner, Admin, Member)
- Set up folder organization — mirror your business structure
- Establish export routines — weekly or monthly data exports to your downstream systems
Common setup mistakes
Choosing the wrong model tier. Standard models on handwritten documents produce frustrating results. Premium models on clean typed PDFs waste credits. Match the model to the document quality.
Extracting too many fields initially. Start with the 5-8 fields you actually need in your downstream system. You can always add more fields later.
Skipping the test phase. Processing 20-30 test documents and reviewing every result catches configuration issues before they affect hundreds of documents.
Setting auto-approve threshold too low. Start at 95% and reduce gradually. It is easier to loosen controls than to fix errors that were auto-approved.
Getting started
Sign up free — 100 credits, no credit card. Upload a few documents of your most common type, create a Smart Flow, and see the extraction quality before committing to a paid plan.