The MongoDB Podcast

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

Read more about Capgemini's Digital Cloud Platform → https://cloud.mongodb.com/ecosystem/c...In this episode of the MongoDB Podcast, Apoorva is joined by Vinay Makkaji from Capgemini and Farid Mohammad from MongoDB to discuss how enterprises are powering the next wave of Agentic AI applications. The conversation explores the shift from AI experimentation to real-world deployment, including AI agents, RAG architectures, and large-scale data modernization.They also unpack how the MongoDB–Capgemini partnership enables organizations to build scalable, production-ready AI solutions through unified data management and modern architectures. Tune in to hear practical use cases, industry examples, and where enterprise AI is headed next.Sign-up for a free cluster → https://www.mongodb.com/cloud/atlas/r...Subscribe to MongoDB YouTube→ https://mdb.link/subscribe
00:00:00 Introduction to the MongoDB Podcast 00:00:58 Meet the Experts: Vinay Makaji & Fared Muhammad 00:03:09 The Three Phases of genAI Evolution 00:04:47 Shifting from Generative to Agentic AI 00:06:55 Why AI is a System, Not Just a Model 00:10:48 The Power of Technology Partnerships 00:17:11 Case Study: Predictive Maintenance in Oil & Gas 00:20:18 How Agentic Systems Prevent $250k/Hour Downtime 00:24:22 The Future: Mainframe Modernization & Industrial IoT 00:28:28 Key Takeaway: Partnerships Build Outcomes 00:30:22 Final Advice: Data Strategy is the Foundation

Are you trying to figure out if your team should build an AI model from scratch or integrate an off-the-shelf solution? You aren’t alone.
In this episode of the MongoDB Podcast, Shane McAlister sits down with Akshaya Murthy, Director of AI Transformation at Zendesk, to decode the maze of building enterprise AI products. They dive into why integrating is often the winning move for speed-to-market, the hidden costs of custom models, and why bad data will break even the most perfect transformer model.

What you’ll learn in this episode:
The Build vs. Buy Calculus: Why lower Total Cost of Ownership (TCO) and rapid deployment favor integration for most enterprises.

Spotting "AI Washing": How to avoid vendor buzzword salads and focus on actual problem-solving and ROI.

Architectural Must-Haves: Why your AI stack needs modular API layers, model hot-swapping, and CI/CD pipelines just like your standard code.

The "Garbage In, Hype Out" Rule: Why a solid data strategy and a centralized single source of truth are non-negotiable.

Ready to stop experimenting and start delivering real AI value? Tune in now.

In this episode, Michael Lynn (MongoDB) and Yang Li (Google Cloud) break down the architectural blueprint for building intelligent, production-grade applications. Move beyond simple RAG (Retrieval-Augmented Generation) and explore the world of AI Agents.
What you’ll learn:
The Google Cloud AI stack: Vertex AI, Agent Space, and Model Garden.

Deep-dive integration: Connecting MongoDB Atlas with BigQuery and Dataflow.

Real-world Demo: Building a grocery store AI assistant using Gemini and Vector Search.

Startup Perks: How to access up to $350k in Google Cloud credits and $10k in MongoDB credits.

Everyone's talking about AI taking over coding jobs, but what's the real story? Shane McAllister and DataCamp's Richie Cotton dive into the "vibe coding" phenomenon and expose the biggest misconceptions developers have about AI. Learn how to shift your mindset from a pure coder to a "vibe curator" and future-proof your career. Don't miss the full video discussion, available to watch now in the Spotify app.