2,000+ Invoices a Month, Processed by an OCR + LLM Pipeline — Accountants Approve, They Don't Type
Line items extracted, categorized to the chart of accounts, and anomaly-checked automatically. 95% extraction accuracy, with the uncertain 5% routed to human review by design.
Written by Shubham(opens in new tab), Founder at BestlaTech · Published May 14, 2026
Engagement Type
AI Document Processing Pipeline
Duration
6 Weeks
Client
US Accounting Firm
Client
US Accounting Firm
Duration
6 weeks
Category
AI Automation / Accounting
Markets
US
Extraction Accuracy
95%
Validated against a 500-document benchmark
Documents/Month
2,000+
Invoices, receipts, and expense documents
Anomaly Screening
100%
Every document checked for duplicates and miscodings
Delivered in Production
6 wks
Fixed scope, fixed price
Key Facts
BestlaTech built an OCR + LLM document processing pipeline for a US accounting firm handling 2,000+ invoices and expense documents a month — extracting line items, auto-categorizing them to each client's chart of accounts, and flagging duplicates and anomalies before posting, with low-confidence documents routed to a human review queue.
Client:
A US accounting and bookkeeping firmWhat was built:
An OCR + LLM pipeline: document ingestion, line-item extraction, chart-of-accounts categorization, anomaly flagging, and a human review queueTimeline:
6 weeks, benchmarked on a 500-document sample before buildEngagement model:
Fixed scope, fixed priceResult:
95% extraction accuracy on real documents; manual keying eliminated for the vast majority of volume; duplicates and miscodings flagged before they reach the ledgerThe Challenge
Manual Keying at 2,000+ Documents a Month
Every scanned invoice and receipt was read and typed into the ledger by hand. Slow, expensive, and growing faster than the team could hire.
Categorization That Depended on Who Was Typing
GL coding varied between staff members. The same vendor could land in different accounts in the same month — and errors only surfaced at reconciliation.
A Month-End Close That Kept Getting Slower
Document backlogs pushed data entry into close week. The team was reconciling and keying at the same time, every month.
Benchmark on Real Documents First. Automate What the Benchmark Proves.
Document AI demos well and fails quietly — on bad scans, odd formats, and edge-case vendors. So before building anything, we benchmarked the extraction pipeline against 500 of the firm's real documents, and designed the system around what the benchmark showed: automate the confident majority, route the rest to humans.
A 500-Document Benchmark Before Build
We tested extraction against real invoices across the firm's actual vendors, formats, and scan quality — so the 95% accuracy figure is measured, not promised.
Extraction Schema Per Document Type
Invoices, receipts, and expense reports each have a defined extraction schema — vendor, dates, line items, tax, totals — returned as validated structured output, not free text.
Categorization Mapped to Each Client's Chart of Accounts
The LLM categorizes line items against the specific client's chart of accounts and historical coding patterns — not a generic category list.
Human Review by Design, Not as a Failure Mode
Low-confidence extractions and flagged anomalies route to a review queue where an accountant approves or corrects in seconds. AI drafts; accountants decide.
Technologies Used
Key Features
Document ingestion via upload and email forwarding, with Celery-based batch processing
OCR extraction with AWS Textract, handling scans, photos, and PDFs
LLM extraction to a validated schema — vendor, dates, line items, tax, totals
Auto-categorization against each client's specific chart of accounts
Duplicate and anomaly detection before anything reaches the ledger
Human review queue with one-click approve/correct for low-confidence documents
Correction feedback loop — every human fix improves the benchmark set
Full audit trail of every extraction, categorization, and correction
Results & Impact
95% extraction accuracy, validated against a 500-document benchmark of real client documents
Manual keying eliminated for the vast majority of 2,000+ monthly documents
Per-document processing time reduced from minutes of typing to seconds of review
Duplicates and coding anomalies flagged before posting instead of surfacing at reconciliation
GL categorization made consistent across the whole team
Delivered in 6 weeks on fixed scope, fixed price
The firm's accountants stopped being data-entry operators. Documents arrive extracted, coded, and anomaly-checked — and the humans do the judgment work: reviewing exceptions, advising clients, and closing the books on time.
Before vs After
| Before | After | |
|---|---|---|
| Data entry | Every document keyed by hand | Automated extraction; humans review exceptions |
| GL categorization | Varied by staff member | Consistent, mapped to each client's chart of accounts |
| Error detection | Weeks later, at reconciliation | Flagged before posting |
| Month-end close | Slowed by data-entry backlog | Documents current daily |
If Your Team Types Data Out of Documents, This Is Solvable.
Invoices, receipts, purchase orders, intake forms — any high-volume document flow with a defined structure is a fit for the same pipeline: OCR, LLM extraction to a schema, confidence-based human review.
Accounting & Bookkeeping Firms
Client document volume grows; your margins shouldn't shrink with it. Extraction plus chart-of-accounts categorization removes the keying, not the accountant.
Finance Teams Drowning in AP
Accounts payable is the classic case: high volume, structured documents, and errors that cost real money. The anomaly flagging alone often pays for the build.
Any Document-Heavy Operation
Insurance claims, loan documents, compliance paperwork — if humans read documents to type what's in them, the same architecture applies.
Your Accountants Should Review Exceptions, Not Type Invoices.
Most discovery calls take 30 minutes. Bring a sample of your documents — by the end, you'll know whether extraction accuracy is achievable on your volume and what a fixed-scope build would involve.
Book Your Free Discovery Call (opens in new tab)Fixed scope. Fixed price. Zero surprises. Serving US, UAE & Singapore.
Frequently asked questions
What does 95% accuracy actually mean — and what happens to the other 5%?
Can it handle poor-quality scans and unusual invoice formats?
Does it integrate with QuickBooks or Xero?
Is our clients' financial data safe going through an LLM?
How long does a build like this take?
Who builds AI invoice processing and accounts payable automation?
Is AI invoice data extraction accurate enough to trust?
Can it categorise invoices to our chart of accounts automatically?
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Get expert advice from our official advisors
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