What challenges did Ellby face before AI implementation?

Template-based OCR systems required significant setup and maintenance for each vendor format change. Ellby needed to lower manual validation, reduce onboarding effort, and improve invoice automation at scale.

Source

Scaled productionEvidence: Medium65/100

Ellby accelerates invoice automation using GenAI on Amazon Bedrock

Use case typeIntelligent document processingUpdated Jun 13, 2026

Ellby, an Australia-based digital solutions company, operates AP automation platforms used by more than 400 businesses across Australia, New Zealand, and Asia Pacific. The company needed to reduce manual effort in onboarding customers and maintaining template-based OCR workflows for changing invoice formats, while improving field-level accuracy and automation.

Organization: Ellby
Industry: Professional Services
Location: Australia
Published: May 2026

Reported outcomes

Automation: More than 60% higher

Automation & deflection

Automation: More than 94% higherTime: More than 55% lowerTime: More than 300 hours

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 60% increase

AWS Solutions case studyMay 13, 2026Customer storyInferred claimMedium evidence strength

Invoice automation increased from under 60% to over 94%.

Normalized claim

Quantified impact: 94% increase

AWS Solutions case studyMay 13, 2026Customer storyInferred claimMedium evidence strength

Invoice automation increased from under 60% to over 94%.

Normalized claim

Time: 55% decrease

AWS Solutions case studyMay 13, 2026Customer storyInferred claimMedium evidence strength

Customer onboarding time was reduced by over 55%.

Normalized claim

Time: 300 hours decrease

AWS Solutions case studyMay 13, 2026Customer storyInferred claimMedium evidence strength

The prior system required more than 300 hours per month for manual maintenance, which the new workflow reduced significantly.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Ellby
Provider: AWS
Maturity: Scaled Production
Linked source: AWS Solutions case study

Ellby needed to lower manual validation, reduce onboarding effort, and improve invoice automation at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Document automation
2Invoice processing
3Accounts payable automation

Ellby embedded generative AI into its EllbyExtract platform using Amazon Bedrock.
The team used Claude Sonnet and Claude Haiku to interpret diverse invoice layouts through intelligent prompts instead of static templates.
A proprietary rules-based engine validates model outputs and flags low-confidence invoices for human review.
Sensitive financial data is processed within AWS infrastructure hosted in Australia to support data residency requirements.

Technologies

Amazon Bedrock Claude Sonnet Claude Haiku

Customer onboarding time was reduced by over 55%.
The prior system required more than 300 hours per month for manual maintenance, which the new workflow reduced significantly.

Architecture

Ellby embedded Amazon Bedrock foundation models into its EllbyExtract invoice automation platform. The system uses prompting to interpret varied invoice formats, applies a proprietary rules-based validation engine to score confidence, and routes low-confidence invoices to human review. The deployment runs within AWS infrastructure in Australia for data residency.

Sources & evidence1

Evidence: Medium65/100Evidence strength