The MongoDB Podcast

Watch this episode as a video on Spotify!
In this episode, Luis Pazmino, Industry Principal for Financial Services at MongoDB, sits down with Saurabh Khandelwal from Capgemini to explore how financial institutions can transform payments data into a strategic revenue engine.
The conversation dives into GenPAL, Capgemini’s solution powered by MongoDB’s modern data platform, and how it enables organizations to move beyond data storage toward real-time intelligence, AI-driven insights, and data monetisation.
In this episode, we discuss:
• Evolving Payments Landscape: Key shifts shaping data strategies and opportunities in payments
• Modern Data Foundations: Building scalable, real-time architectures to support high-volume transactions and compliance needs
• From Data to Value: Turning payments data into actionable insights and new revenue streams
• AI at Scale: Leveraging gen AI and agentic AI for fraud detection, customer intelligence, and operational efficiency
• Pragmatic Modernization: How to adopt AI and modern data platforms without a “big bang” approach
Whether you’re a fintech innovator or a financial institution navigating legacy systems, this episode offers practical insights on unlocking the full value of payments data with AI and modern data platforms.

Timestamps
00:09 – Introduction

01:05 – Introducing GenPAL

02:38 – The tactical shift: From standard BI to Agent AI

04:56 – Identifying strains

06:59 – Solving payment failures

08:48 – Sub-millisecond latency

12:48 – The "Experience Gap"

15:01 – Strategic advice

18:14 – High-level architecture

21:35 – Real-world success

24:09 – Future outlook

Watch this episode in video format on Spotify!
If you're building Python applications on MongoDB and still writing raw queries by hand, you're leaving a lot of developer productivity on the table. Beanie, the async-first ODM built on Pydantic, was created to fix exactly that — and this episode goes deep on how and why it works.
You'll learn how Beanie maps Python objects to MongoDB documents without sacrificing atomicity or performance, why async-first design matters for modern Python stacks, how schema migrations actually work in a document database, and what the deprecation of Motor means for your existing codebase. The episode also covers Beanie's integration with FastAPI, how it handles indexes and aggregation pipelines under the hood, and what's coming in the next phase of the library.
Ramon, the creator of Beanie and a senior software engineer at Microsoft, built this library five years ago to fill a gap nobody else had addressed. He's joined by Shubham, MongoDB's product manager for Python client libraries, for a live demo and Q&A.
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[00:00] Introduction & Guest Welcome
[01:00] What Is Beanie? The ODM Explained
[04:10] ODM vs ORM — What's the Difference?
[05:20] Why Ramon Built Beanie (The Origin Story)
[06:30] Core Design Principles: Atomicity & Async-First
[08:00] FastAPI + MongoDB: The Rising Python Stack
[11:00] Bonnet: The Synchronous Beanie Backport
[12:55] Live Demo: Defining Document Schemas with Pydantic
[16:00] Nested Documents, Links & Polymorphic Collections
[18:45] Best Practices for Schema Design
[20:30] Index Management in Beanie
[22:40] Complex Queries: Beanie vs Raw PyMongo
[24:30] Aggregation Pipelines in Beanie
[28:05] Schema Migrations: Forward, Backward & Freefall
[31:30] Motor Is Deprecated — What That Means for You
[34:00] Beanie v2: What Changed and What Didn't
[36:20] FastAPI, Flask & Django Integration
[37:45] What's Next for Beanie: Performance & Lambda Optimization
[39:30] How to Contribute to Beanie
[41:00] Resources, Community & Audience Q&A

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.