From 7 Days to 2 Minutes: Automating Workflows with Knowledge Graphs

Are you still relying on OCR for your enterprise AI? You're losing critical context. In this episode, Anaiya Raisinghani (Sr. Tech. Evangelist, AI Startups & Ventures at MongoDB) sits down with Adityavardhan Agrawal, Co-Founder and CEO of Morphik. They dive deep into how Morphik is helping developers and enterprises understand complex, unstructured data and automate high-leverage workflows. Adi breaks down the limitations of standard RAG pipelines and reveals why they turned to Vision Language Models (VLMs) to process complex documents like architectural floorplans. What you’ll learn in this episode: The OCR Trap: Why text extraction is inherently lossy for complex documents and how VLMs generate better embeddings. The RAG Misconception: Why getting high-quality context requires much more than just plain vector search. Database Architecture: Why Morphik hit the limits of Postgres/JSONB for dynamic datasets and how migrating to MongoDB Atlas simplified their multi-tenancy and querying. Massive ROI: How one manufacturing customer used Morphik to slash their quote generation time from 7 days to under 2 minutes. The Future of Knowledge: Building self-healing, self-updating data layers that leverage MQL. (Want to start building? You can use Morphik's API, Python/TypeScript SDKs, or grab the Docker image from GitHub today!) ⏱️ Chapter Timestamps 00:00 - Intro: Meet Adi and Morphik 01:18 - APIs, SDKs, and Getting Started with Morphik 02:28 - The Lightbulb Moment: Why Standard AI Fails on Unstructured Data 04:44 - The Biggest Misconception About RAG 06:24 - Vision Language Models (VLMs) vs. Traditional OCR 08:35 - Reducing Entropy: Combining Embeddings with Knowledge Graphs 10:13 - Architecture Deep-Dive: Hitting the Limits of Postgres & JSONB 12:06 - Why Morphik Migrated to MongoDB Atlas 13:24 - Simplifying Multi-Tenancy at Scale 15:13 - Ensuring Data Security and Reliability 16:33 - Accelerating Growth with MongoDB for Startups 18:10 - Real-World Impact: Cutting Quote Generation from 7 Days to 2 Minutes 20:15 - The Future: Self-Healing Data Layers and Native MQL