Read more → https://mdb.link/9Sh4hgiNBdc-morphik Explore the future of enterprise AI with Adi Agrawal, co-founder and CEO of Morphik, as he sits down with Anaiaya Raisinghani from MongoDB Ventures. In this interview, they dive deep into the limitations of traditional Retrieval-Augmented Generation (RAG) and why equating RAG solely with vector search is a major industry misconception. Adi explains how Morphik moves beyond lossy OCR and textual embeddings by using Vision Language Models (VLMs) to help businesses understand complex, unstructured data—from architectural drawings to industrial manuals. Discover why Morphik pivoted from PostgreSQL to MongoDB Atlas to handle dynamic metadata, simplify multi-tenancy, and reduce engineering overhead. Learn about a real-world manufacturing use case where Morphik reduced a 10-day quoting process to under two minutes, and hear Adi’s vision for a "self-healing" data layer. Subscribe to the MongoDB for Developers YouTube Channel: https://www.youtube.com/@MongoDBDevelopers?sub_confirmation=1 00:00:00 Intro: Meet Adi, CEO of Morphik 00:01:13 Morphik: Enterprise Solutions vs. Dev Tools 00:02:29 The Challenge of Unstructured Data in AI 00:03:35 Why Basic RAG Tools Often Fail 00:04:54 Debunking the RAG vs. Vector Search Myth 00:05:58 Using Vision Language Models (VLMs) for Context 00:08:48 Transforming Entropy into Structured Information 00:09:59 Why Morphik Moved from Postgres to MongoDB Atlas 00:13:34 Simplifying Multi-Tenancy for Enterprise Scaling 00:14:58 Solving the "Trust Problem" in AI Data 00:17:59 Real-World Case Study: Industrial Quoting at Scale 00:20:00 The Future of Self-Healing Data Layers Sign-up for a free cluster → https://www.mongodb.com/cloud/atlas/register Subscribe to MongoDB YouTube→ https://mdb.link/subscribe Visit Mongodb.com → https://mdb.link/MongoDB Read the MongoDB Blog → https://mdb.link/Blog Read the Developer Blog → https://mdb.link/developerblog MongoDB for Developers YouTube Channel → https://www.youtube.com/@MongoDBDevelopers

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