Invoice processing · Production

Lano automates global payroll document processing with super.AI IDP, achieving 92%+ extraction accuracy

The problem

Lano's growing volume of international pay slips and invoices overwhelmed its in-house invoice automation (high error rates, constant maintenance overhead) and its fully manual pay slip processing (slow and error-prone across diverse international formats), eroding team morale and customer satisfaction.

First attempt

Lano's in-house invoice automation carried high error rates and maintenance overhead that made it unsustainable, and several months of testing market alternatives revealed each was too limited in scope.

Workflow diagram · grounded in source
1
Customer documents arrive
trigger
“processing steadily increasing volumes of customer documents, specifically pay slips and invoices”
2
IDP data extraction
ai_action
“Lano dramatically transformed its document processing workflow with super.AI's Intelligent Document Processing (IDP) solution”
3
Automated results delivered
output
“Lano has fully automated processing complex international pay slips and invoices, eliminating the lags, errors, and bottlenecks associated with manual document handling”
Reported outcome

super.AI IDP fully automated Lano's document processing at 100% automation rate with 92%+ out-of-the-box extraction accuracy across 10+ languages and 50+ layouts, achieving a near-zero error rate and dramatically shorter process times while elevating customer satisfaction.

Reported metrics
Error ratenear-zero error rate
Process timesdramatically shorter
Reported stack
super.AI
Source
https://super.ai/case-studies/lano
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

super.AI IDP fully automated Lano's document processing at 100% automation rate with 92%+ out-of-the-box extraction accuracy across 10+ languages and 50+ layouts, achieving a near-zero error rate and dramatically shor…

What tools did this team use?

super.AI.

What results were reported?

Error rate: near-zero error rate; Process times: dramatically shorter (source-reported, not independently verified).

What failed first in this deployment?

Lano's in-house invoice automation carried high error rates and maintenance overhead that made it unsustainable, and several months of testing market alternatives revealed each was too limited in scope.

How is this invoice processing AI workflow structured?

Customer documents arrive → IDP data extraction → Automated results delivered.