Invoice extractor
Invoice extractor
The Invoice Extractor is a fully automated invoice processing pipeline that reads invoices from emails, extracts key fields using AI, validates them against business rules, and posts clean, structured data into ERP/SAP. It eliminates manual data entry, reduces errors, and provides endtoend traceability for finance and compliance teams.- End-to-end flow:
- Email →
- S3 →
- AI extraction →
- Validation →
- ERP / SAP posting →
- Audit trail
- Business teams served:
- Finance,
- Accounts Payable,
- Vendor Management
Problem Statement/Definition
The finance team was manually downloading invoice attachments from emails, entering data into ERP/SAP, and performing validations, leading to heavy operational overhead. This process was slow, error-prone, and could not scale with increasing invoice volumes.- 15–20 minutes required to process a single invoice manually.
- High error rates causing payment mistakes and vendor disputes.
- Peak load bottlenecks when invoice volumes spiked at month-end.
- Finance resources stuck in repetitive tasks instead of analysis and strategic work.
Proposed Solution and Architecture
The solution uses a serverless, event-driven architecture on AWS to automate the complete invoice lifecycle from email capture to ERP posting. It combines OCR, AI-based validation, and robust integration components to deliver a scalable and low-maintenance platform.
Ingestion
➤ AWS SES receives invoice emails and routes attachments to Amazon S3.
➤ S3 events trigger downstream processing in a fully automated fashion.
Data extraction and validation
➤ Amazon Textract extracts line items, totals, vendor details, tax components, and other key invoice fields.
➤ AWS Bedrock models validate fields, handle format variations, and enforce business rules for accuracy and completeness.
Storage and integration
➤ Validated invoice records are stored in DynamoDB for fast querying, audit trails, and reporting.
➤ An application layer on AWS Elastic Beanstalk pushes clean, validated data into ERP/SAP through secure APIs/integration connectors.
Reliability and operations
➤ Event-driven architecture ensures high scalability and low idle costs.
➤ Error handling and human-in-the-loop mechanisms allow manual review of complex or ambiguous invoices.
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Outcome of Project and Success Metrics
The Invoice Extractor delivered a step change in efficiency, accuracy, and throughput for accounts payable operations.
- ➤ 90% reduction in manual data entry effort per invoice.
- ➤ Processing time reduced from 15–20 minutes to under 2 minutes per invoice.
- ➤ Accuracy improved from 85% to 98%, dramatically reducing payment errors and disputes.
- ➤ Throughput increased to 500+ invoices per day with the same team size (≈300% improvement).
- ➤ Average vendor payment cycle reduced by around 5 days, improving vendor satisfaction and relationships.
- ➤ Finance staff redeployed from repetitive data entry to higher-value financial analysis and decision support.
TCO Analysis Performed
A three-year Total Cost of Ownership (TCO) comparison showed that automation significantly outperformed the legacy manual process.
Legacy (manual) cost profile
➤ Annual spend ≈ 180,000 USD including labor, error correction, penalties, and email infrastructure.
➤ Hidden costs from delayed payments, disputes, and lack of scalability.
Automated solution cost profile
➤ Year 1 cost ≈ 45,000 USD including AWS services, development, and integration.
➤ Subsequent annual run cost ≈ 30,000 USD for cloud services and maintenance.
Financial impact
➤ Payback period of around 8 months.
➤ Projected savings of approximately 375,000 USD over 3 years.
➤ Additional benefits in cash flow, compliance posture, and scalable growth without proportional cost increases.
Lessons Learned
The implementation surfaced several best practices for scaling AI-powered document processing across diverse vendor formats.
- ➤ Technical and design learnings
- Invest early in flexible validation rules to handle varied invoice layouts and vendor formats.
- Iteratively tune Textract configurations and Bedrock prompts based on real-world invoice samples.
- Maintain detailed audit logs in DynamoDB to support troubleshooting, compliance, and ROI tracking.
- ➤ Delivery and rollout learnings
- Involve end users (AP and finance teams) from the beginning for UAT, edge-case discovery, and process alignment.
- Use a phased rollout by vendor segments to reduce risk and continuously refine the solution.
- Implement robust error handling and human-in-the-loop review paths to build trust and ensure continuity during exceptions.
Our office Address
Head Office
B 1302,1303, Sun Westbank, Ashram Rd, Vishalpur, Muslim Society, Navrangpura, Ahmedabad, Gujarat 380009
Branch Office
Raheja Platinum, Sag Baug Road, off, Andheri – Kurla Rd, Marol, Andheri East, Mumbai, Maharashtra
