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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. Real-Time Kafka Streaming Analytics System
Industry: Technology, Software, Data Analytics Duration: 5 Months Project Cost: $9,500 Started On: June 2024 Developed a real-time event streaming and analytics platform for processing high-volume transactional data with minimal latency. The client needed a scalable architecture capable of handling live business events, processing streaming data in real time, and generating operational insights instantly. I designed a distributed Kafka-based event pipeline and developed Python consumers for processing and transforming incoming data streams. The system included fault-tolerant stream processing, automated monitoring, Docker-based deployment pipelines, and scalable microservices architecture orchestrated through Kubernetes. Key achievements: • Built a scalable event-driven architecture • Enabled real-time analytics and monitoring • Improved system reliability and fault tolerance • Reduced latency in data processing operations Technologies: Apache Kafka, Python, Docker, Kubernetes, PostgreSQL, GitLab CI/CD 
