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MongoDB.local San Francisco 2026: Ship Production AI, Faster
January 15, 2026
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Redefine Airline Loyalty: Innovation for the Modern Traveler
Driven by shifts in traveler behavior across the globe, airline loyalty is undergoing a profound transformation. Travelers now expect better digital services from airlines. Retailers, streaming platforms, mobility apps, and financial services applications have raised expectations for what a personalized, easy, and meaningful experience should feel like. Those expectations are also carried over into the travel industry.
Heidi’s AI Scribe Scales to 81M Clinical Consultations
Clinicians spend up to 40% of their working hours on documentation tasks. Heidi, a rapidly growing Australian-based global AI startup, is on a mission to protect and extend the human touch in healthcare. In just 18 months, Heidi has returned more than 18 million hours to frontline clinicians by streamlining critical administrative tasks.
Unlock Historical Archive Value with Multimodal AI
Digitization was supposed to solve the archive problem. Scan the pages, run Optical Character Recognition (OCR), enable keyword search—done. Yet decades and millions of dollars later, most newspaper archives remain essentially unusable for serious research.
Now Source Available: The Engine Powering MongoDB Search
Last year, we announced the public preview of search and vector search capabilities for use with MongoDB Community and MongoDB Enterprise Server. Today, we are releasing the engine that powers those capabilities, mongot, into public preview under the Server Side Public License (SSPL).
Semantic Power, Lexical Precision: Advanced Filtering for Vector Search
We are excited to announce the launch of Lexical Prefilters for MongoDB Vector Search, providing developers with a way to use advanced text and geo analysis filters alongside vector search. This new capability simplifies the challenge of building advanced applications by helping developers build richer and more sophisticated search experiences through the combination of semantic understanding and precise text filtering.
Introducing the Embedding and Reranking API on MongoDB Atlas
The next frontier for AI isn’t simply more capable models. It’s better context. As LLMs become embedded in every process and product, their accuracy and trust depend on grounding generation in the right data. Search and retrieval are foundational to this shift, powering everything from AI chatbots and assistants to fully autonomous agents. But building AI retrieval today means stitching together databases, vector search, and retrieval model providers—each introducing operational complexity.
Unlocking AI Search: Introducing Automated Embedding in MongoDB Vector Search
We are excited to announce the public preview of Automated Embedding in MongoDB Vector Search (available in Public Preview in MongoDB Community Edition), a groundbreaking feature designed to make building sophisticated, AI-powered applications easier than ever. Since 2023, MongoDB has offered integrated vector search alongside our operational database, and last year we announced the acquisition of Voyage AI, bringing state-of-the-art embedding and reranking models to our customers.
Control MongoDB Atlas Cluster Configurations With Resource Policies
Editor's note: This post is the fifth in a series exploring how MongoDB Atlas Resource Policies help you strengthen database security and enforce consistent guardrails across your organization. Read the first post on IP access list management, the second on defense-in-depth strategies, the third on blocking wildcard IP access, and the fourth on cloud provider and region restrictions.
Teach & Learn: Dr. Mahesh Chaudhari, University of San Francisco
This is the fourth in our Teach & Learn blog series, which interviews students and educators worldwide who are using MongoDB to enhance their classrooms. These stories highlight how MongoDB’s platform and resources are revolutionizing education and preparing tech professionals.