See #Nexty AI and @nextdataOS autonomous data products in action via the clip below 👇 #AutonomousDataProducts #AIAgents #DataMesh #generativeAI #nextdataOS

⚡Introducing Nexty AI: Self-governed, standardized data products generated automatically from your legacy code on @nextdataOS 🧵👇 #AutonomousDataProducts #AIAgents #DataMesh #generativeAI #nextdataOS

Simple, speedy, safe. In just minutes, you can spin up a fully autonomous, self-governing, AI-ready autonomous data product. Want your own demo? Click here. nextdata.com/get-a-demo

📅Mark your calendars for #DataMeshLive in Antwerp, June 5–6, 2025! Thrilled to announce that @nextdataOS Head of Engineering, @joerg_schad, will be giving the keynote on how to go from legacy stacks to an operational Data Mesh in today’s #GenAI & #ML era. 👇

Ever wonder what it takes to be a data startup founder? 🤔 Hear @nextdataOS CEO @zhamakd on the newest episode of Born In Silicon Valley, where she shares her journey from Iran to Silicon Valley & her vision for decentralized data products. 🔗: hubs.la/Q0382MB90

Ever feel like you're trapped in an endless cycle of building, cataloging, and re-building data pipelines? In our latest video, @nextdataOS CEO @zhamakd shares a bold new approach to data management 👇 #datamesh #autonomousdataproducts

🔍The Hidden Costs of Partial Data Mesh Implementations 1/7 Many claim to have “adopted” data mesh—but too often, it’s only a half-measure. Our latest blog dives into the hidden costs of partial implementations. 🧵 👇 #DataMesh #DataTransformation

👉 Watch the entire MeshRAG webinar recording here: youtu.be/cfc2gFnFS80 Let us know what resonated with you in the comments! #MeshRAG #DataMesh #GenAI 6/6

🚀 Simplifying RAG Applications with MeshRAG : 🧵👇 1/5 In traditional ML, #dataproducts transformed data management with a multi-modal approach, ensuring consistency, quality, and scalability. These standards are essential for apps that rely on embeddings (e.g. #RAG Apps)

Why Is Building RAG Applications Challenging at Enterprise Scale? 🧵👇 Everyone knows #RAG applications are the easiest way to train #LLMs with custom data, but most examples only showcase a single pipeline approach.