Problem: Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.

Solution: This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.

Very real problem. Manual invoice processing is still surprisingly common and becomes a huge bottleneck as companies scale. Automating extraction and validation with AI can save a lot of operational time and reduce errors significantly.

Clean Asset Management Dashboard 🙂‍↕️

‍✅ Instant portfolio health check.

✅ Metrics separated by Assets, Financials, Risk.

✅ Expense trends show what's changing and why.

✅ Critical dates flagged by urgency. Everything you need to make decisions, so operators see what matters and act fast.