If your BPO team processes invoices, receipts, IDs, application forms, shipping docs, or vendor statements every day, you already know the problem:
- Documents arrive in different formats
- Teams copy/paste into sheets
- Fields get missed, columns drift, and QA takes forever
- Scaling means hiring more people (and still fighting errors)
Img2Sheet was built for this exact workflow: turn messy documents into consistent Google Sheets rows—at scale—using AI extraction (not basic OCR).
Why “AI extraction” beats “OCR”
Traditional OCR only gives you text. That’s not enough when you need:
- “Invoice Total” (not every number on the page)
- “Vendor Name” (not the header text)
- “Due Date” (not every date present)
- “Line Items” mapped into the right columns
- Consistent outputs across different templates and layouts
AI extraction focuses on meaning, not just text. That means you tell the system what you want, and it returns structured values that match your sheet.
The core idea: your structure becomes your spreadsheet schema
Instead of praying OCR lands in the right place, you define your structure once, then run batches through it.
1) Create a structure (your extraction template)
You define columns with:
- Label (e.g., “Invoice Number”, “Total”, “Currency”, “Vendor”)
- Type (
textornumber) - Prompt (what to extract exactly — in plain language)
Example structure for invoices:
- Vendor Name (text) — “Extract the company name that issued the invoice.”
- Invoice Number (text) — “Extract the invoice reference number.”
- Invoice Date (text) — “Extract the invoice issue date.”
- Total Amount (number) — “Extract the grand total payable (not subtotal).”
- Tax Amount (number) — “Extract total tax/VAT amount, if present.”
This “structure” is your single source of truth.
2) Upload documents to extract against that structure
Once the structure is created, your team uploads document images (or scans).
The system uses AI to extract the exact fields you asked for.
3) Data lands in Google Sheets in the same column order
Every extracted document becomes a new row, following your structure exactly.
No column mismatch. No shifting fields. No “where do I paste this value?”
What this looks like for a BPO operation
Use case 1: Accounts payable processing
Batch-process hundreds of supplier invoices daily and push results into one Google Sheet:
- Faster invoice indexing
- Fewer manual entry mistakes
- Cleaner handoff to accounting / ERP import
Use case 2: Receipt & expense operations
Extract merchant, date, total, VAT, payment method into Sheets:
- Standardized expense logs
- Easier monthly reconciliation
- Real-time reporting dashboards
Use case 3: Onboarding & applications
Convert forms into rows:
- Shorter turnaround time
- Consistent columns for follow-up workflows
- Reduced QA overhead
Built for quality control, not just speed
BPO work isn’t “extract anything”—it’s “extract the right thing consistently.”
That’s why the structure includes prompts per column. Prompts let you be strict:
- “Return only the numeric value, no currency symbols”
- “If not present, return blank”
- “Use YYYY-MM-DD format”
- “Extract the buyer’s company name, not the seller’s”
This is how you reduce edge-case chaos without rebuilding the workflow.
Security note: documents don’t stay stored
Many BPO teams deal with sensitive documents.
Important:
- We do not provide file links after processing
- All uploaded files are removed immediately after extraction
You keep the output in Google Sheets, not the originals.
Getting started: a simple BPO rollout plan
- Pick one document type (e.g., invoices)
- Create one structure (your columns + prompts)
- Run a batch and review output in Sheets
- Adjust prompts and column types once
- Scale to other document types (receipts, statements, forms)
If you’re processing hundreds of documents daily…
Your team shouldn’t be doing repetitive copy/paste work.
With AI extraction + structures mapped directly to Google Sheets, you get:
- Faster throughput
- More consistent output
- Lower QA workload
- A workflow that scales without scaling headcount