Invoice processing · Production

Uber advances invoice document processing using GenAI with the TextSense platform

The problem

Uber's invoice processing relied on manual data entry and RPA that could not scale to diverse invoice formats, invoices arriving in over 25 languages, and high volumes, leading to high average handling time, errors, and rising operational costs.

First attempt

Existing Rule-Based Systems and RPA could not adapt to new invoice formats without manual rule-setting, failed to scale as Uber onboarded new suppliers and document formats, and required continual maintenance and manual error correction.

Workflow diagram · grounded in source
1
Invoice submission trigger
trigger
“There are two methods for suppliers to submit invoices”
2
Document ingestion
integration
“Integrates documents from sources like emails, PDFs, and ticketing systems, and saves files in an object storage platform. Supports structured and unstructured data formats.”
3
Pre-processing
ai_action
“Includes image augmentation to handle low-resolution scans and handwritten texts.Converts document formats (PDFs, Word documents, images) into a standard format suitable for processing.”
4
OCR text extraction
ai_action
“Uses Uber's Vision Gateway CV platform for optical character recognition to extract text from document images.”
5
LLM data extraction
ai_action
“Leverages trained or pre-trained LLM models for extracting specific data elements like invoice numbers, dates, and amounts.Continuously improves through periodic re-training and feedback loops to address accuracy issues and adapt to new …”
6
Post-processing and validation
validation
“Applies business rules and user-defined post-processing steps to refine extracted data before final use.Ensures data quality by cross-referencing with existing databases or predefined rules.”
7
HITL review
human_review
“We designed a UI to enable users performing the HITL (Human in the Loop) review to do a side-by-side comparison of the PDF data versus the data extracted from the models”
8
ERP integration and payment
integration
“the documents are processed as invoices and sent to the ERP system for approval and vendor payments”
9
Metrics and feedback loop
feedback_loop
“Captures key performance indicators like processing speed, accuracy rates, and cost efficiency.Uses these metrics to drive continuous improvements.”
Reported outcome

The GenAI-powered TextSense system achieved a 2x reduction in manual invoicing, an overall accuracy rate of 90%, a 70% reduction in average handling time, and a 25-30% cost saving compared to the manual process.

Reported metrics
Manual invoicing reduction2x reduction
Overall accuracy rate90%
Invoices achieving near-perfect accuracy (99.5%)35%
Invoices achieving above 80% accuracy65%
Show all 7 reported metrics
manual invoicing reduction2x reduction
overall accuracy rate90%
invoices achieving near-perfect accuracy (99.5%)35%
invoices achieving above 80% accuracy65%
average handling time reduction70%
cost saving vs manual process25-30%
invoice languages handledover 25 languages
Reported stack
TextSenseVision GatewayOCRNLPGPT-4CadenceRPALlama 2Flan T5ERP system
Source
https://www.uber.com/en-IN/blog/advancing-invoice-document-processing-using-genai/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The GenAI-powered TextSense system achieved a 2x reduction in manual invoicing, an overall accuracy rate of 90%, a 70% reduction in average handling time, and a 25-30% cost saving compared to the manual process.

What tools did this team use?

TextSense, Vision Gateway, OCR, NLP, GPT-4, Cadence, RPA, Llama 2, Flan T5, ERP system.

What results were reported?

Manual invoicing reduction: 2x reduction; Overall accuracy rate: 90%; Invoices achieving near-perfect accuracy (99.5%): 35%; Invoices achieving above 80% accuracy: 65% (source-reported, not independently verified).

What failed first in this deployment?

Existing Rule-Based Systems and RPA could not adapt to new invoice formats without manual rule-setting, failed to scale as Uber onboarded new suppliers and document formats, and required continual maintenance and manu…

How is this invoice processing AI workflow structured?

Invoice submission trigger → Document ingestion → Pre-processing → OCR text extraction → LLM data extraction → Post-processing and validation → HITL review → ERP integration and payment → Metrics and feedback loop.

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