Agentic integration is the practice of building the data and pipeline layer specifically for AI agents, so agents can discover, request, and act on enterprise data conversationally instead of through hand-coded pipelines.
Nexla’s Agentic Integration pillar spans Express.dev, Agentic RAG, and Helix Context Layer, so agents get grounded, governed data instead of hallucinating from ungoverned raw sources.
A research-backed framework for evaluating LLM-generated data transformations. Learn how datasets, sandboxed execution, and automated judging reveal failure patterns and model performance across real-world data engineering tasks.
Explore how Express.dev makes AI agents capable of generating rich, interactive UI for structured data workflows. From XML-driven forms to real-time validation and OAuth flows, generative UI turns chat into a truly collaborative experience.
After years of solving data variety, we built Express, a conversational data engineering platform that turns complex data work into simple, prompt-driven pipelines making data engineering accessible to everyone.
Most data teams waste time fixing brittle pipelines instead of driving insights. See how AI-powered transformation and Nexla’s Common Data Model cut manual work and ensure scalable pipelines.
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.
Explore how businesses can overcome data bottlenecks, ensure seamless integration, and unlock the full potential of Generative AI at scale.
Discover how Nexla and NVIDIA simplify AI adoption with seamless data integration, accelerated GenAI workflows, and scalable solutions for business success.
Retrieval-Augmented Generation (RAG) is reshaping how organizations use GenAI to access and synthesize information, supporting…
In today’s fast-paced GenAI landscape, organizations are constantly searching for smarter, more efficient ways to…
Taking a Retrieval-Augmented Generation (RAG) solution from demo to full-scale production is a long and…
From GenAI prototypes to production: the contributions of integration engineers in model management, vector pipelines, RAG workflows, GPT quality control, & LLM governance.
