Introducing 
Express — Go from prompt to pipeline in minutes.
Governed self-service data embeds metadata controls, quality guardrails, and access policies. This enables business users to explore and transform data in no-code while preventing metric drift.
Customer API and CSV feeds create engineering bottlenecks. Learn how to standardize raw customer data into governed, reusable data products using Common Data Models—eliminating custom integrations and scaling onboarding.
Raw feeds without context create endless rework. This metadata-first blueprint shows how to turn changing source feeds into governed, reusable data products with automated validation, lineage, and GenAI-ready contracts.
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.
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.
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.
A podcast for data leaders looking for real conversations on AI adoption and data architecture, hosted by Nexla CEO Saket Saurabh.
Nexla brings 500+ pre-built data connectors to Microsoft 365 Copilot, enabling organizations to easily integrate internal and third-party data for smarter AI workflows.
In this episode of Software Engineering Radio, listen how the Model Context Protocol is transforming AI system architecture, and empowering developers with agentic workflows
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 choose the right automated ETL tools for your startup. Discover key selection factors, compare top platforms, and find solutions that scale with your business growth.
When data quality drops, revenue follows. Automated ETL fixes that by eliminating errors, enforcing standards, and ensuring consistency across systems to deliver trustworthy analytics and business insights.
