Data entry ops · Production

Nanonets OCR automates Spanish receipt processing for Advantage Marketing Group across Latin America

The problem

Advantage Marketing Group was manually processing 100,000 receipts per month with 60 data keyers, spending 2–3 days preparing documents, while existing data extraction technology could not handle low-resolution or differently oriented receipt images.

Workflow diagram · grounded in source
1
Receipts arrive from multiple sources
Trigger
Receipts arriving from multiple sources, including faxed and low-resolution images, initiate the digitization workflow.
▾ source quote
“keeping a track of receipts coming in from multiple sources and digitizing them to strategize incentives was becoming cumbersome”
2
Smart preprocessing pipeline
Ai action
An on-premise smart data processing pipeline is deployed to capture only relevant information from receipt images.
▾ source quote
“we deployed an on-premise smart data processing pipeline to capture only relevant info”
3
OCR model extracts receipt fields
Ai action
An OCR model processes Spanish receipts to capture fields including Address, Total, Date, Ticket Number, Retailer, Product Name, Description, and Line Item Total.
▾ source quote
“Nanonets delivered an OCR model enabled to process Spanish receipts to capture numerous fields - Address, Total, Date, Ticket Number, Retailer, Product Name, Description, and Line Item Total”
4
API integration with marketing platform
Integration
Advantage Marketing integrates the Nanonets API with their own marketing platform.
▾ source quote
“Advantage Marketing easily integrated the API with their own marketing platform to demonstrate to a larger client base”
Reported outcome

Within 7 days, Nanonets delivered an OCR model for Spanish receipts; Advantage Marketing integrated the API with their marketing platform and demonstrated the solution to a larger client base.

Reported metrics
Receipts processed per brand per month20,000
Total receipts manually indexed per month100,000
Data keyers employed for manual indexing60
Time spent preparing documents pre-automation2-3 days
Show all 5 reported metrics
receipts processed per brand per month20,000
total receipts manually indexed per month100,000
data keyers employed for manual indexing60
time spent preparing documents pre-automation2-3 days
model delivery time7 days
Reported stack
NanonetsOCR modeldocker apitheir own marketing platform
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Source
https://nanonets.com/customer-success-story/how-advantage-marketing-a-marketing-platform-benefitted-from-nanonets
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Within 7 days, Nanonets delivered an OCR model for Spanish receipts; Advantage Marketing integrated the API with their marketing platform and demonstrated the solution to a larger client base.

What tools did this team use?

Nanonets, OCR model, docker api, their own marketing platform.

What results were reported?

Receipts processed per brand per month: 20,000; Total receipts manually indexed per month: 100,000; Data keyers employed for manual indexing: 60; Time spent preparing documents pre-automation: 2-3 days (source-reported, not independently verified).

How is this data entry ops AI workflow structured?

Receipts arrive from multiple sources → Smart preprocessing pipeline → OCR model extracts receipt fields → API integration with marketing platform.

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