In today’s rapidly evolving digital landscape, organizations across industries are reimagining how work gets done. The demand for speed, accuracy, and continuous adaptability has surpassed the capabilities of traditional automation systems. Businesses are now turning toward intelligent, self-coordinating ecosystems that can sense, reason, and act autonomously.

As organizations increasingly adopt AI-driven solutions to enhance efficiency and intelligence in operations, one challenge consistently emerges —scalability. Traditional automation platforms, while efficient for static or rule-based workflows, often crumble under the pressure of dynamic workloads and context-driven decision-making.

In the rapidly expanding world of artificial intelligence, enterprises are increasingly demanding accuracy, explainability, and reliability from their AI systems. While Large Language Models (LLMs) have shown immense potential, they often struggle with factual correctness — a phenomenon known as hallucination. The solution lies in Retrieval-Augmented Generation (RAG), a framework that combines the reasoning ability of LLMs with the factual strength of external knowledge sources.