Nexla

About Nexla

Nexla provides an enterprise-grade, AI-powered data integration platform that transforms data from any source into production-ready data products for artificial intelligence (AI) applications and agents. The company was founded in 2016 by CEO Saket Saurabh, CPO Niket Sourabh, CTO Avinash Shahdadpuri, and Jeff Williams. The founding team's vision was to simplify and automate complex enterprise data preparation, a significant bottleneck they observed, to make data instantly usable. CEO Saket Saurabh is a repeat entrepreneur with a background in engineering at NVIDIA and as a founder at mobile ad-serving firm Mobsmith. His experience in building large-scale, real-time data systems and observing how infrastructure enables transformative applications was a key driver behind Nexla's creation. Headquartered in San Mateo, California, Nexla has raised a total of $33.5 million over three funding rounds, including a Series B in July 2023. Its investors include firms like Industry Ventures, Shield Capital, and Blumberg Capital. The company operates on a subscription-based business model, serving enterprise clients across industries such as finance, retail, and healthcare. Notable customers include Autodesk, DoorDash, Johnson & Johnson, and LinkedIn. Nexla's core product is a no-code/low-code platform that unifies data integration, preparation, governance, and monitoring. It features over 700 pre-built, bi-directional connectors and supports all integration styles, including ELT, ETL, streaming, APIs, and Retrieval-Augmented Generation (RAG) for generative AI. A key innovation is the creation of 'Nexsets,' which are virtual, human-readable data products. These Nexsets are automatically generated and include semantic metadata, data quality validation, and lineage tracking, allowing both technical and non-technical users to collaborate. The platform is designed to be deployed as a SaaS, in a hybrid multi-cloud environment, or on-premises, and it processes over a trillion records per month. Nexla's focus on creating an automated and governed data layer for AI agents aims to reduce deployment times from months to days.