Resources | MongoDB

May 1, 2025

People Who Ship Ep 1: Building Centralized Gen AI Tooling

12 p.m. ET Join us for the first episode of our “People Who Ship” series, where you'll learn how to build sophisticated, production-grade AI applications with MongoDB. In this live episode, you will hear from Apoorva Joshi, Senior AI Developer Advocate and John Ziegler, Engineering Lead on MongoDB's internal generative AI tooling team. They will share the architecture, technical decisions, and practical lessons learned while developing centralized infrastructure that empowers teams at MongoDB to rapidly build RAG-based chatbots and copilots for diverse use cases. You'll learn: The technical architecture behind MongoDB's central RAG platform Key design decisions that enabled scalability across multiple use cases Implementation challenges and how they were overcome Evaluation and feedback mechanisms The episode will also include a live demo of the tooling in action, showing how teams are using Central RAG to rapidly deploy chatbots for their unique use cases. We promise no fluff. No sales pitches. Just an honest, technical conversation about building real AI systems that deliver value. Register now to secure your spot.

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May 7, 2025

Building Deep Research Gen AI Tooling With Credal and MongoDB

11 a.m. ET Go-to-market teams rely on deep research to identify high-value prospects and craft personalized outreach. But manual research processes are time-consuming, inefficient, and often incomplete. This is about to change. Join Ria Balli (Founding Engineer, Credal) and Richmond Alake (Staff Developer Advocate - AI/ML, MongoDB) on May 7th, at 11:00 a.m. EST, for a webinar that explores how Credal and MongoDB are transforming enterprise research workflows with AI-powered automation. Learn how to deploy a deep research agent that automatically scans public profiles and internal CRM data to pinpoint key prospects, gathers contextual insights into each contact’s pain points, and generates highly personalized outreach messages in minutes, all while maintaining compliance and security standards for sensitive data. Why attend this session? Understand how AI automation reduces research time from days to minutes. Learn how to securely integrate public and private data sources. See a live demo of Credal’s deep research tooling powered by MongoDB. Gain insights from industry experts on the future of AI-driven GTM strategies. Don’t miss this opportunity to revolutionize your research and outreach strategy with AI. Register now!

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Building RAG Applications Using Multimodal Data

AI search must evolve beyond text-only approaches to handle the diverse, multimodal content found across the internet. To build truly effective retrieval-augmented generation (RAG) applications, you need strategies that process text, images, tables, and figures seamlessly. In this on-demand session, MongoDB Senior AI Developer Advocate Apoorva Joshi explores advanced techniques for integrating multimodal data into your RAG pipelines. Watch the session to learn: Multimodal embedding techniques for diverse data formats (text, images, tables, and figures) How Voyage AI's multimodal embedding models differ from competitors and drive more accurate retrievals Using Vision Language Models (VLMs) as an alternative to traditional chunking approaches Comparative evaluation of Voyage multimodal models against other solutions for mixed-modality data Advanced strategies for exploring and retrieving from multimodal datasets Discover how MongoDB and Voyage AI empower AI applications to retrieve and process information across multiple modalities. Watch now to take your multimodal AI search to the next level!

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April 30, 2025

Ready to revolutionize how you build and modernize applications?

11 a.m. ET Join us for an engaging and educational webinar, “Enhance developer agility and AI-readiness with MongoDB Atlas on Azure,” where we’ll showcase our cloud-native, AI-ready database that seamlessly integrates with the Microsoft Intelligent Data Platform. Join MongoDB and Microsoft for this live webinar to discover how your team can: Accelerate development and enhance scalability with our intuitive, flexible database that simplifies building, iteration, and deployment. Power AI-driven applications by integrating MongoDB Atlas with Azure AI and analytics services, including Azure AI Foundry and Microsoft Fabric. Strengthen your security posture through native integrations with Microsoft Entra ID, Azure Key Vault, and Microsoft Purview. Build without boundaries using the Microsoft Intelligent Data Platform while maintaining the freedom to run anywhere. During this session, our technical experts will demonstrate how MongoDB Atlas on Azure helps you reduce complexity and build on a unified database platform that helps you differentiate your business from your competition.

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April 29, 2025

Automate Regulatory Compliance with Advanced AI and MongoDB

12 p.m. ET Join us for an exclusive webinar with ADA as we show how to use cutting-edge AI technologies and MongoDB's powerful database capabilities to create an AI-powered compliance monitoring system. While the solution addresses compliance management specifically, the approaches and techniques demonstrated apply to many AI application areas. Ryan Stephens and Kosta Djukic are Principal Engineers at ADA, the Canadian company that used AI-powered automations to transform customer service. They’ll join MongoDB Staff Developer Advocate Richmond Alake in this practical, solution-focused session. They’ll show you how to: Build an automated document ingestion pipeline to handle unstructured data across multiple channels and formats. Implement MongoDB Atlas as both an operational and vector database. Use Voyage AI embedding models for semantic understanding of regulations. Apply content moderation model ShieldGemma 9B to evaluate transaction compliance. Use change streams to iterate on and train models in real-time. Orchestrate the entire workflow with LangGraph for scalable, state-managed processing. Join us to level up to your AI skill sets.

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April 24, 2025

Scaling Vector Database Operations with MongoDB and Voyage AI

11:30 a.m. SGT The performance and scalability of your AI application depend on efficient vector storage and retrieval. In this webinar, we explore how MongoDB Atlas Vector Search and Voyage AI embeddings optimize these aspects through quantization—a technique that reduces the precision of vector embeddings (e.g., float32 to int8) to decrease storage costs and improve query performance while managing accuracy trade-offs. Vector embeddings are the foundation of AI-driven applications, along with powerful capabilities such as retrieval-augmented generation (RAG), semantic search, and agent-based workflows. However, as data volumes grow, the cost and complexity of storing and querying high-dimensional vectors increase. Join Senior Staff Developer Advocate Anant Srivastava to learn how quantization improves vector search efficiency. We’ll cover practical strategies for converting embeddings to lower-bit representations, balancing performance with accuracy. In a step-by-step tutorial, you'll see how to apply these optimizations using Voyage AI embeddings to reduce both query latency and infrastructure costs. Key Takeaways: How quantization works to dramatically reduce the memory footprint of embeddings How MongoDB Atlas Vector Search integrates automatic quantization to efficiently manage millions of vector embeddings Real-world metrics for retrieval latency, resource utilization, and accuracy across float32, int8, and binary embeddings Combining binary quantization with a rescoring step yields near float32-level accuracy with a fraction of the computational overhead Best practices and tips for balancing speed, cost, and precision—especially at the 1M+ embedding scale essential for RAG, semantic search, and recommendation systems

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April 24, 2025

Intro to Memory Management in AI Agents

12 P.M. ET AI agents go beyond retrieval-augmented generation (RAG) to transform how intelligent systems learn, adapt — and remember. So managing how agents store, recall, and process remembered data is essential to delivering seamless, hyper-personalized, and context-rich interactions. Join MongoDB Staff Developer Advocate Richmond Alake for this engaging webinar. It will equip you with the knowledge and practical tools to integrate advanced memory systems into your projects. We’ll break down key concepts and practical techniques to ensure your agents remember what matters, adapt to new information, and behave in ways that truly resonate with users. What You’ll Learn: How agentic RAG extends traditional RAG by integrating memory management into agent workflows. The essentials of memory management — how to design, build, and evolve an agent’s memory. The core components and building blocks of effective AI memory. RAG techniques and hybrid search approaches that combine lexical and vector methods for fast and accurate AI retrieval. Practical strategies for implementing memory management using MongoDB’s flexible document model, robust indexing, and native vector search capabilities. Register now and take the first step toward building AI agents that truly think ahead!

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AI Database Comparison: MongoDB Atlas vs. Elasticsearch

Large language models (LLMs) are revolutionizing AI application development, from retrieval-augmented generation (RAG) to intelligent agentic systems that dynamically reason, learn, and adapt. The AI ecosystem offers a range of technologies to build these solutions—but one of the most critical elements is the database. In generative AI applications, your database directly impacts response latency, application performance, and output accuracy. This session compares two solutions for vector data storage, indexing, and retrieval: MongoDB Atlas and Elasticsearch. We break down how each handles semantic search, provide performance insights, and walk through best practices for implementing vector search in MongoDB Atlas. Watch MongoDB Staff Developer Advocate Richmond Alake as he guides you through: Common search patterns in AI workloads with concrete examples and detailed performance guidance for MongoDB Atlas Vector Search. Why database architecture matters in generative AI, illustrated through real-world implementation scenarios that showcase developer productivity and application capabilities. How to implement MongoDB Atlas Vector Search step-by-step, with live coding demonstrations that show how to enable semantic search for powerful RAG solutions through a unified developer experience. Practical walkthrough of building complete RAG pipelines that integrate real-time data, with working code examples you can adapt for your own dynamic LLM-driven applications. Actionable best practices for optimizing MongoDB Atlas for AI workloads, including live demonstrations of index configuration, efficient query patterns, and practical scaling strategies. Whether you're building AI chatbots, recommendation engines, or agentic systems, understanding how your database powers LLM-enabled applications is essential. Watch now to gain practical knowledge and working code examples that will help you design and optimize AI-powered solutions with MongoDB Atlas.

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Application Modernisation: From Relational to NoSQL with Relational Migrator

We've heard time and time again that applications running on legacy relational databases cause technical and business pain, but there is a fear that modernizing them is too difficult and risky. MongoDB’s Relational Migrator significantly reduces the effort and risk of moving to a modern data architecture. In addition to data migration, it helps you model your schemas efficiently in MongoDB, update application code, and even convert existing code with the help of AI. The webinar will be presented by Cathlon Lau, Advisory Solutions Architect at MongoDB, who has decades of experience working with RDBMS, PostgreSQL, and document databases like MongoDB. Register now and find out why it’s essential to consider database platforms specifically designed to handle large, complex data structures to effectively support modern AI applications and learn how MongoDB’s Relational Migrator can significantly reduce the effort and risk of transitioning to a modern data architecture.

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April 22, 2025

Adopting AI-Driven Transformation with MongoDB, Capgemini, and AWS

11 A.M. EST Join us to explore how an AI-powered integration between MongoDB, Capgemini, and Amazon Web Services (AWS)’s Digital Cloud Platform enhances customer engagement, streamlines operations, and drives growth across industries. Join the webinar to learn how to: Seamlessly integrate AI-driven capabilities into operations and core applications. Move beyond legacy limitations and unify data across disparate, siloed systems. Accelerate application development and shrink time-to-market with prebuilt accelerators for industry-specific domains. Use AWS services, like Amazon Lex and AWS Lambda to enable personalized, scalable conversational AI across channels. This webinar is ideal for organizations looking to explore how AI, cloud technologies, and next-gen data management are reshaping industries, delivering agile, scalable, and personalized solutions that drive growth and enhance customer experiences. Speakers: Igor Alekseev from AWS, Abhi Chakraborty from MongoDB, and Vinay Makkaji from Capgemini. Date & Time: Tuesday, April 22nd at 11 AM EST

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April 29, 2025

AI Database Comparison: MongoDB vs. PostgreSQL and Pgvector

10 A.M. BST Large language models (LLMs) are rapidly reshaping how we build AI solutions. The database technology you choose to power these applications can significantly impact the performance, scalability, and success of your AI application. In this webinar, Staff Developer Advocate Richmond Alake will compare two vector search solutions—PostgreSQL with pgvector and MongoDB Atlas Vector Search—and guide you through selecting the right option for your AI workloads. Whether you’re a data engineer, AI architect, or developer, you’ll understand how to think about and optimize critical metrics like latency and throughput to meet the demands of modern AI applications. What you’ll learn: How retrieval-augmented generation (RAG) boosts LLM-based applications by integrating external data in real time. How PostgreSQL/pgvector and MongoDB Atlas handle high-performance vector operations for tasks like semantic search and recommendation engines. How robust vector databases enable AI agents to reason, plan, and act autonomously, creating truly dynamic and interactive AI experiences. How a real-world application using a financial Q&A dataset illustrates practical deployment and optimization strategies. How key metrics like latency and throughput directly affect the success of LLM applications. Reserve your seat now to be part of this engaging and thought-provoking discussion. Even if you can’t attend, register anyway and we’ll send you a link to watch on-demand and at your convenience. Hope to see you there!

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Intro to AI Agents and Agentic Systems

During this session, Staff Developer Advocate Richmond Alake explored the transformative impact of AI agents and agentic systems, providing invaluable insights and hands-on examples to help you build powerful AI-driven applications. Key topics included: Constructing AI Agents powered by LLMs Developing agentic systems for complex workflows Interacting with databases, including querying MongoDB Connecting to external tools and retrieving real-time data Optimizing systems for scalability and performance The webinar also featured an engaging question-and-answer session, where MongoDB experts shared guidance, offered pro tips, and discussed best practices for leveraging these emerging technologies.

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