Turning Manual Receipt Entry Into a Competitive Advantage
A PE backed company replaced a 10 person manual receipt entry team with a vision and language pipeline, cutting per receipt processing cost by more than 90 percent and moving to same day processing.
>90% cost cut
< 4 weeks
Implementation Time
Not disclosed
Project Cost
the challenge
The company processed thousands of receipts each week from field operations and vendors, with a 10 person offshore team keying required fields into the ERP. Batch turnaround often lagged by days, delaying close and cash visibility. Manual entry introduced errors that needed downstream reconciliation, and costs scaled linearly with volume, so growth demanded more headcount.
what they built
Eliza built a vision and language pipeline that combines OCR and layout parsing with LLM extraction. Confidence based QA routes low confidence receipts to human review while routine ones process automatically. Schema aware validation enforces business rules for dates, taxes, GL mappings, currency, and line item reconciliation. PII is redacted, data is encrypted in transit and at rest with full audit logging, and the system drops in through SFTP or a watch folder and an API connector to the existing database.
The pipeline was built to drop into existing systems with minimal change, routing only low confidence cases to the existing team while processing routine receipts straight through. First value came within three weeks.
best fit for
Finance and operations teams processing high volumes of receipts or invoices manually, especially PE backed companies looking for fast cost takeout and cleaner, faster close.
Ai ROLE
AI reads each receipt with OCR and layout parsing, extracts and validates fields with an LLM against business rules, and auto-processes routine items while routing edge cases to people.
infrastructure
ERP system
SFTP/watch folder ingestion
API database connector
integration points
Drop-in integration through SFTP or a watch folder
API connector to the existing ERP
Confidence-based routing
Low-confidence receipts sent to human review
Routine receipts processed automatically
impact
>90% lower cost
More than 90 percent reduction in per receipt processing cost versus the manual offshore team
Same-day
Multi day batch turnaround replaced by same day straight through processing
EBITDA lift
Immediate EBITDA lift and faster cash visibility through quicker vendor reconciliation
Brian Benedict
Co-Founder & Chief Commercial Officer
Eliza
Co-founder of Eliza, a boutique AI transformation firm and OpenAI Advanced Tier Partner. A two-time founder and former Hugging Face, he helps enterprises put AI to work through agentic engineering.
How did a company automate manual receipt data entry at enterprise scale?
The experts built a vision and language pipeline that pairs OCR and layout parsing with LLM extraction, confidence based QA, and schema aware validation. It cut per receipt processing cost by more than 90 percent and moved the work to same day straight through processing.
What AI tools and approach were used for receipt processing?
The pipeline combines OCR and layout parsing with LLM based extraction in a document processing approach, with confidence based routing, schema aware validation, and drop in integration through SFTP and an API connector to the existing ERP.
What results did the company achieve?
More than 90 percent reduction in per receipt processing cost, multi day batches replaced by same day straight through processing, and an immediate EBITDA lift with faster cash visibility.
How long did it take to see results?
First value came within about three weeks.
Who is this receipt automation approach best for?
Finance and operations teams processing high volumes of receipts or invoices manually, especially private equity backed companies looking for fast cost takeout and a cleaner close.
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