Beyond Vector Search: Scaling the AI Data Layer with Morphik & MongoDB Atlas

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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.

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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

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Видео Beyond Vector Search: Scaling the AI Data Layer with Morphik & MongoDB Atlas канала MongoDB