AI systems fail when context doesn’t scale. This article explains the limits of context graphs, why static relationships break for enterprise AI, and what’s needed to deliver accurate, trustworthy AI outputs at scale.

AI is shifting data engineering from code-heavy ETL to prompt-driven pipelines. Explore where LLMs fit, common pitfalls, and how Nexla makes AI-ready data workflows practical.

While it is true that AI offers enormous opportunities for innovation and success, its reliance on personal data raises urgent concerns about privacy, ethics, and governance

Nexla, the leading integration platform for AI applications, today announced the launch of Express, a conversational data engineering platform that makes it simple for anyone to work with data.

Learn how to transform your data into AI-ready assets. Understand the components of AI-ready data and how Nexla’s data product capabilities empower your AI initiatives.

At the 2025 Data + AI Integration Summit, a conversation between Amey Desai (CTO at Nexla) and Qingyun Wu (Founder & CEO at AG2) unpacked how multi-agent systems and orchestration frameworks are shaping the next generation of enterprise AI.

By combining an intelligent orchestration layer with a robust runtime engine, organizations can scale their AI integration capabilities while maintaining operational control.

Nexla contributes key innovations from its cutting-edge agentic AI framework to the open source community, reinforcing its commitment to advancing enterprise-grade AI technology.

Major release simplifies AI-ready data delivery, expands agentic retrieval to improve accuracy, introduces no-code data engineering assistant, and adds Data Product Marketplace to govern AI.