We build robust, scalable AI-driven data pipelines leveraging distributed computing frameworks such as Apache Spark, TensorFlow, and Keras. Our architecture spans Retrieval-Augmented Generation (RAG) and GraphRAG frameworks built on Large Language Models (LLMs), utilizing LangChain, LangGraph, and the Model Context Protocol(MCP) to power agentic workflows, semantic enrichment, and contextual inference.

Containerized workloads are orchestrated via Kubernetes across hybrid environments—including on-premise clusters and cloud platforms such as AWS, Azure, GCP, and Databricks. We implement MLOps pipelines for continuous integration and deployment, apply GPU-accelerated training for performance optimization, and maintain real-time observability for system reliability. These capabilities drive high-throughput, fault-tolerant solutions tailored for complex AI/ML workloads and enterprise governance.

Big data demands resilient, scalable infrastructure. At BigCodeGen, we design and deploy high-performance ELT/ETL pipelines and AI/ML-driven data architectures that empower organizations to extract value from the exponential growth of data generated by customers, connected products, and digital services. Our solutions harness cloud-native platforms, distributed processing frameworks, and intelligent automation to transform raw data into actionable insights. This empowers organizations to unlock new revenue streams, optimize operational efficiency, and deliver hyper-personalized experiences—driving growth, profitability, and predictive decision-making at scale.