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Automated Ticket Invoice Processing & Analytics Using AWS Textract and Redshift
A media and event-driven organization managing high-volume ticket sales receives periodic ticket invoices via email from multiple vendors and booking partners. These invoices arrived in semi-structured and unstructured formats (PDFs, scanned images, email attachments), making manual processing time-consuming, error-prone, and non-scalable. To address this, we designed and implemented a fully automated, serverless data pipeline on AWS that ingests invoices from email, extracts structured data using OCR, Custom Scripts, and NLP, stores curated datasets in Amazon S3, and loads analytics-ready data into Amazon Redshift. This enabled descriptive analytics and downstream ticket sales forecasting with strong governance, observability, and cost efficiency.
Customer Challenges
The client faced multiple operational and analytical challenges in managing high-volume ticket invoice data across vendors and events.
Solutions
To automate invoice processing and enable scalable analytics, an event-driven, serverless data pipeline was implemented using managed AWS services. The solution automated the end-to-end lifecycle of ticket invoice ingestion, extraction, validation, and analytics enablement.
Ticket invoice attachments received via email were automatically ingested and stored in Amazon S3. Each file was captured with complete metadata and audit attributes, ensuring traceability from ingestion through downstream processing.
AWS Textract was used to extract structured data from unstructured and semi-structured invoice documents, including scanned PDFs and images. This enabled reliable extraction of invoice headers, line items, taxes, and totals without relying on rigid, vendor-specific templates.
A layered data lake was designed in Amazon S3 to support data quality, traceability, and reprocessing:
- Bronze Layer: Stored original invoice files to preserve source fidelity and support audit and replay scenarios.
- Silver Layer: Applied data extraction, validation, normalization, and vendor-specific transformations using modular Python scripts.
Invoice data was standardized across vendors into a standard schema. Business rules were applied to validate totals, taxes, and line items, ensuring consistent and accurate invoice data for downstream analytics and reporting.
Curated, analytics-ready invoice datasets were loaded into Amazon Redshift. Tables were optimized for time-series and event-level analysis, enabling fast descriptive reporting, ticket sales trend analysis, and performance benchmarking.
The platform enabled downstream use cases such as event-level ticket revenue forecasting and vendor performance analysis by providing a centralized, historical invoice dataset.
Operational reliability and governance were enforced through centralized logging, monitoring, and alerting using AWS-native services. Role-based access controls ensured secure, governed access to invoice and sales data for finance and analytics stakeholders.
Services Used
Why choose NeenOpal?
NeenOpal brings strong expertise in building cloud-native, serverless data platforms on AWS, combining data engineering, document intelligence, and analytics at scale. With hands-on experience in OCR, NLP-driven extraction, and governed data architectures, NeenOpal helps organizations automate complex, unstructured data workflows. The team’s focus on reliability, observability, and business outcomes ensures faster insights, reduced operational effort, and a future-ready analytics foundation.
Benefits
The automated invoice processing and analytics platform delivered significant gains in efficiency, accuracy, scalability, and decision-making across finance and operations.
Conclusion
This solution transformed a manual, fragmented invoice process into a scalable, governed, analytics-ready data platform. By combining OCR-driven document intelligence with modern data lake and warehouse architecture, the client unlocked faster insights, improved forecasting accuracy, and significantly reduced operational overhead. The architecture is vendor-agnostic, extensible, and ready to support additional document types, new ticketing partners, and advanced machine learning use cases.
