Introducing 
Express — Go from prompt to pipeline in minutes.
Essential checklist for validating AI-ready data before building LLM pipelines. Learn the 10 critical steps ML teams must follow to ensure quality, freshness, and compliance.
Context engineering is the systematic practice of designing and controlling the information AI models consume at runtime, ensuring outputs are accurate, auditable, and compliant.
In the News: betanews.com: In this Q&A, Saket Saurabh explains why context engineering is key to reliable, compliant, and intelligent enterprise AI workflows.
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.
In the News: InfoQ recently featured Nexla Express in a detailed article on conversational AI for data engineering. The piece highlights how Express enables users to create AI-ready pipelines using plain language.
In the News: Saket Saurabh, Nexla CEO, explains why enterprise AI’s future lies in context engineering: building the right data, memory, and workflows for smarter, more reliable AI.
In this episode of Software Engineering Radio, listen how the Model Context Protocol is transforming AI system architecture, and empowering developers with agentic workflows
In the News: Watch Nexla CEO Saket Saurabh analyze the Fivetran-dbt Labs merger with John Furrier on theCUBE + NYSE Wired, exploring its strategic impact on the evolving data ecosystem.
In the News: Saket Saurabh sits down with Joe Reis to discuss why context, not models, defines competitive advantage in AI and how enterprises must rethink data strategy.
Dive into Apache Iceberg’s benefits. Reliable pipelines need strong operations, from catalog options to lakehouses. Learn about time travel, schema evolution, and best practices for scalable maintenance with Nexla.
Discover how Apache Iceberg separates storage from compute to improve modern data lake performance. See how Nexla supports CDC into Iceberg and also uses Iceberg as a source to streamline data integration and analytics.
