Gregory Maxson

9 results

Building Gen AI with MongoDB & AI Partners | October 2024

It’s no surprise that AI is a topic of seemingly every professional conversation and meeting nowadays—my friends joke that 11 out of 10 words that come out of my mouth are “gen AI.” But an important question remains: do organizations truly know how to harness AI, or do they simply feel pressured to join the crowd? Are they driven by FOMO more than anything else? One thing is for sure: adopting generative AI still presents a huge learning curve. Which is why we’ve been working to provide the right tools for companies to build innovative gen AI apps with, and why we offer organizations a variety of AI knowledge and guidance, regardless of where they are with gen AI. We’re fortunate to work with our industry-leading partners to help educate and shape this nascent market. Working so closely with them on product launches, integrations, and solving real-world challenges allows us to bring diverse perspectives and a better understanding of AI to our customers, giving them the technology and confidence to move forward even before engaging with tough use cases and specific technical problems (something that the MongoDB AI Applications Program can definitely help with). One of our main educational initiatives has been our webinar series with our top-tier MAAP partners. We’ve constantly launched video content to deepen understanding of topics essential to gen AI for enterprises answering broader questions such as “ how can my company generate AI-driven outcomes ” and “ how can I modernize my workload ,” to specific, tangible topics such as “ how to build a chatbot that knows my business .” Each session is designed to move beyond the basics, sharing insights from experts in AI, and addressing our customers’ burning questions and challenges that matter most to them. Welcoming new AI and tech partners In October, we also welcomed four new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! Astronomer Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading data orchestration and observability platform powered by Apache Airflow. " Astronomer's partnership with MongoDB is redefining RAG workflows for GenAI workloads. By integrating Astronomer's managed Apache Airflow platform with MongoDB Atlas' powerful vector database capabilities, we enable organizations to orchestrate complex data pipelines that fuel advanced AI and machine learning applications”, said Julian LaNeve, CTO at Astronomer. “This collaboration empowers data teams to manage real-time, high-dimensional data with ease, accelerating the journey from raw data to actionable insights and transforming how businesses harness the power of generative AI." CloudZero CloudZero is a cloud cost optimization platform that automates the collection, allocation, and analysis of cloud costs to identify savings opportunities and improve cloud efficiency rates. "Database spending is one of the shared costs that can make it tricky for organizations to reach 100% cost allocation. CloudZero eliminates that problem," said Anand Sundaram, Senior Vice President of Product at CloudZero. “ Our industry-leading allocation engine can organize MongoDB spend in a matter of hours , tracing it precisely to the products, features, customers, and/or teams responsible for it. This way, companies get a clear view of what’s driving their costs, who’s accountable, and how to optimize to maximize their cloud efficiency.” ObjectBox ObjectBox is an on-device vector database for mobile, IoT, and embedded devices that enables storing, syncing, and querying data locally online and offline. " We’re thrilled to partner with MongoDB to give developers an edge,” celebrated Vivien Dollinger, CEO and co-founder of ObjectBox. “By combining MongoDB’s cloud and scalability with ObjectBox’s high-performance on-device database and data sync, we empower developers to build fast, data-rich applications that feel right at home across devices and environments. Offline, online, edge, cloud, whenever, wherever... We’re here to enable your data with speed and reliability." Rasa Rasa is a flexible framework for building conversational AI platforms that lets companies develop scalable generative AI assistants that hit the market faster. “ Rasa is excited to partner with MongoDB to empower companies in building conversational AI experiences. Together, we’re helping create generative AI assistants that save costs, speed up development, and maintain full brand control and security,” said Melissa Gordon, CEO of Rasa. “With MongoDB, deploying production-ready generative AI assistants is seamless, and we’re eager to continue accelerating our customers’ journey toward trusted conversational AI solutions.” But wait, there's more! Whether you’re starting out or scaling up, MongoDB and our partners are here with the resources, expertise, and trusted guidance to help you succeed in your genAI strategy! And if you have any suggestions for a good webinar topic, don’t hesitate to reach out. To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

November 11, 2024

MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024

Building Gen AI with MongoDB & AI Partners | September 2024

Last week I was in London for MongoDB.local London —the 19th stop of the 2024 MongoDB.local tour—where MongoDB, our customers, and our AI partners came together to share solutions we’ve been building that enable companies to accelerate their AI journey. I love attending these events because they offer an opportunity to celebrate our collective achievements, and because it’s great to meet so many (mainly Zoom) friends in person! One of the highlights of MongoDB.local London 2024 was the release of our reference architecture with our MAAP partners AWS and Anthropic , which supports memory-enhanced AI agents. This architecture is already helping businesses streamline complex processes and develop smarter, more responsive applications. We also announced a robust set of vector quantization capabilities in MongoDB Atlas Vector Search that will help developers build powerful semantic search and generative AI applications with more scale—and at a lower cost. Now, with support for the ingestion of scalar quantized vectors, you can import and work with quantized vectors from your embedding model providers of choice, including MAAP partners Cohere, Nomic, and others. A big thank you to all of MongoDB’s AI partners, who continually amaze me with their innovation. MongoDB.local London was another great reminder of the power of collaboration, and I’m excited for what lies ahead as we continue to shape the future of AI together. As the Brits say: Cheers! Welcoming new AI and tech partners In September we also welcomed seven new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! Arize Arize AI is a platform that helps organizations visualize and debug the flow of data through AI applications by quickly identifying bottlenecks in LLM calls and understanding agentic paths. "At Arize AI, we are committed to helping AI teams build, evaluate, and troubleshoot cutting-edge agentic systems. Partnering with MongoDB allows us to provide a comprehensive solution for managing the memory and retrieval that these systems rely on”, said Jason Lopatecki, co-founder and CEO of Arize AI. “With MongoDB’s robust vector search and flexible document storage, combined with Arize’s advanced observability and evaluation tools, we’re empowering developers to confidently build and deploy AI applications." Baseten Baseten provides the applied AI research and infrastructure needed to serve custom and open-source machine learning models performantly, scalably, and cost-efficiently. " We're excited to partner with MongoDB to combine their scalable vector database with Baseten's high-performance inference infrastructure and high-performance models. Together, we're enabling companies to build and deploy generative AI applications, such as RAG apps, that not only scale infinitely but also deliver optimal performance per dollar,” said Tuhin Srivastava, CEO of Baseten. “This partnership empowers developers to bring mission-critical AI solutions to market faster, while maintaining cost-effectiveness at every stage of growth." Doppler Doppler is a cloud-based platform that helps teams manage, organize, and secure secrets across environments and applications that can be used throughout the entire development lifecycle. “Doppler rigorously focuses on making the easy path, the most secure path for developers. This is only possible with deep product partnerships with all the tooling developers have come to love. We are excited to join forces with MongoDB to make zero-downtime secrets rotation for non-relational databases effortlessly simple to set up and maintenance-free,” said Brian Vallelunga, founder and CEO of Doppler. “This will immediately bolster the security posture of a company’s most sensitive data without any additional overhead or distractions." Haize Labs Haize Labs automates language model stress testing at massive scales to discover and eliminate failure modes. This, alongside their inference-time mitigations and observability tools, enables the risk-free adoption of AI. " We're thrilled to partner with MongoDB in empowering companies to build RAG applications that are both powerful yet secure, safe, and reliable,” said Leonard Tang, co-founder and CEO of Haize Labs. “MongoDB Atlas has streamlined the process of developing production-ready GenAI systems, and we're excited to work together to accelerate customers' journey to trust and confidence in their GenAI initiatives." Modal Modal is a serverless platform for data and AI/ML engineers to run and deploy code in the cloud without having to think about infrastructure. Run generative AI models, large-scale batch jobs, job queues, and more, all faster than ever before. “The coming wave of intelligent applications will be built on the potent combination of foundation models, large-scale data, and fast search,” explained Charles Frye, AI Engineer at Modal. “MongoDB Atlas provides an excellent platform for storing, querying, and searching data, from hot new techniques like vector indices to old standbys like lexical search. It's the perfect counterpart to Modal's flexible compute, like serverless GPUs. Together, MongoDB and Modal make it easy to get started with this new paradigm, and then they make it easy to scale it out to millions of users querying billions of records & maxing out thousands of GPUs.” Portkey AI Portkey AI is an AI gateway and observability suite that helps companies develop, deploy, and manage LLM-based applications. " Our partnership with MongoDB is a game-changer for organizations looking to operationalize AI at scale. By combining Portkey's LLMOps expertise with MongoDB's comprehensive data solution, we're enabling businesses to deploy, manage, and scale AI applications with unprecedented efficiency and control,” said Ayush Garg, Chief Technology Officer of Portkey AI. “Together, we're not just streamlining the path from POC to production; we're setting a new standard for how businesses can leverage AI to drive innovation and deliver tangible value." Reka Reka offers fully multimodal models including images, videos with audio, text, and documents to empower AI agents that can see, hear, and speak. "At Reka, we know how challenging it can be to retrieve information buried in unstructured multimodal data. We are excited to join forces with MongoDB to help companies test and optimize multimodal RAG features for faster production deployment,” said Dani Yogatama, CEO of Reka. “Our models understand and reason over multimodal data including text, tables, and images in PDF documents or conversations in videos. Our joint solution streamlines the whole RAG development lifecycle, speeding up time to market and helping companies deliver real values to their customers faster." But wait, there's more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub , and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

October 9, 2024

Building Gen AI with MongoDB & AI Partners | August 2024

As the AI landscape continues to evolve, companies, industries, and developers seek tailored solutions to their unique challenges. Gone are the days when general-purpose AI models could be applied universally. Now, organizations are looking for industry-specific applications, verticalized AI solutions, and specialized tools to gain a competitive edge and best serve their customers. And as gen AI use cases have diversified—from healthcare diagnostics and autonomous driving, to personalized recommendations and creative content generation—so has the technology stack supporting them. The complexity of building and deploying AI models has led to the rise of specialized AI frameworks and platforms that streamline workflows and optimize performance for specific use cases. In this context, having the right AI stack is essential for driving innovation. AI development is no longer just about choosing the best model but also about selecting the right tools, libraries, and infrastructure to support that model across the board. All of which makes partnerships (and combining technical strengths) increasingly important to innovating with AI. Take, for example, our most recent integration with LangChain: the MongoDB-LangChain partnership exemplifies how having the right components in an AI stack allows teams to focus on innovating instead of managing infrastructure bottlenecks. By combining LangGraph with MongoDB’s vector search capabilities, developers can create more sophisticated, high-performing AI applications. This integration allows for the seamless development of agentic AI systems capable of generating actionable insights and delivering complex tasks. To learn more about building powerful AI agents with LangGraph.js and MongoDB, plus our recent work making vector search even more versatile with custom LangChain Retrievers, check out our tutorial . Welcoming new AI partners MongoDB’s partnership with LangChain highlights the importance of building adaptable solutions that can grow and change as the needs of developers and customers grow and change. Which is why MongoDB is always on the lookout for innovative partners and solutions—in August we welcomed five new AI partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! BuildShip BuildShip is a low-code visual backend and workflow builder to instantly create APIs, scheduled tasks, backend cloud jobs, and automation, powered by AI. " We at BuildShip are thrilled to partner with MongoDB to introduce an innovative low-code approach for rapidly building AI workflows and backend tasks in a visual and scalable manner,” said Harini Janakiraman, CEO of BuildShip.com. “MongoDB offers a comprehensive data stack for AI developers and organizations, enabling them to efficiently build scalable databases and access vector or hybrid search options for their products. Our collaboration provides customizable low-code templates that allow for easy integration of MongoDB databases with a variety of AI models and tools. This enables teams and companies to quickly build powerful APIs, automations, vector search, and scheduled tasks, unlocking organizational efficiency and driving product innovation.” Inductor Inductor is a platform to prototype, evaluate, improve, and observe LLM apps and features, helping developers ship high-quality LLM-powered functionality rapidly and systematically. “ We’re excited to partner with MongoDB to enable companies to rapidly create production-grade LLM applications, by combining MongoDB's powerful vector search with Inductor’s developer platform enabling streamlined, systematic workflows for developing RAG-based applications,” said Ariel Kleiner, CEO of Inductor. “While many LLM-powered demos have been created, few have successfully evolved into production-grade applications that deliver business wins. Together, Inductor and MongoDB enable enterprises to build impactful, needle-moving LLM applications, accelerating time to market and delivering real value to customers.” Metabase Metabase is the easy-to-use, open source Business Intelligence tool that lets everyone work with data, with or without SQL, for internal and customer-facing, embedded analytics. "This partnership is an important step forward for NoSQL database analytics. By integrating Metabase with MongoDB , two popular open-source tools, we are making it easier for users to quickly get valuable insights from their MongoDB data,” explained Luiz Arakaki, Product Manager at Metabase. “Our goal is to create a better integration between the tools to offer more advanced features and stability, simplifying the use of NoSQL databases for advanced analytics.” Shakudo Shakudo is a comprehensive development platform that lets data professionals develop, run, and deploy data pipelines and applications in an all-in-one integrated environment. “ Shakudo is thrilled to be partnering with MongoDB to streamline the entire retrieval-augmented generation (RAG) development lifecycle. Together we help companies test and optimize their RAG features for faster PoC, and production deployment,” noted Yevgeniy Vahlis, CEO of Shakudo. “MongoDB has made it dead simple to launch a scalable vector database with operational data, and Shakudo brings industry leading AI tooling to that data. Our collaboration speeds up time to market and helps companies get real value to customers faster.” VLM Run VLM Run is a versatile API that enables accurate JSON extraction from any visual content such as images, videos, and documents, helping users to integrate visual AI to applications. “ VLM Run is excited to partner with MongoDB to help enterprises accurately extract structured insights from visual content such as images, videos and visual documents,” said Sudeep Pillai, Co-Founder and CEO of VLM Run. “Our combined solution will enable enterprises to turn their often-untapped unstructured visual content into actionable, queryable business intelligence.” But wait, there's more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub , and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem. Head over to our quick-start guide to get started with Atlas Vector Search today.

September 11, 2024

Building Gen AI with MongoDB & AI Partners | July 2024

My colleague Richmond Alake recently published an article about the evolution of the AI stack that breaks down the “comprehensive collection of integrated tools, solutions, and components designed to streamline the development and management of AI applications.” It’s a good read, and Richmond—who’s an AI/ML expert and developer advocate—explains clearly how the modern AI stack evolved from a set of disparate tools to the (beautifully) interdependent ecosystem on which AI development relies today. “The modern AI stack represents an evolution from the fragmented tooling landscape of traditional machine learning to a more cohesive and specialized ecosystem optimized for the era of LLMs and gen AI,” Richmond writes. In other words, this cohesive ecosystem is aimed at ensuring end-to-end interoperability and seamless developer experiences, both of which are of utmost importance when it comes to AI innovation (and software innovation overall). Empowering developer innovation is exactly what MongoDB is all about—from streamlining how developers build modern applications, to the blog post you’re reading now, to the news that the MongoDB AI Applications Program (MAAP) is now generally available. In particular, the MAAP ecosystem represents leaders from every part of the AI stack who will provide customer service and support, and who will work with them to ensure smooth integrations—with the ultimate aim of helping them build gen AI applications with confidence. As the saying goes, it takes a village. Welcoming new AI partners Because the AI ecosystem is constantly evolving, we're always working to ensure that customers can seamlessly integrate with the latest cohort of industry-leading companies. In July we welcomed nine new AI partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! Enkrypt AI Enkrypt AI secures enterprises against generative AI risks with its comprehensive security platform that detects threats, removes vulnerabilities, and monitors performance for continuous insights. The solution enables organizations to accelerate AI adoption while managing risk and minimizing brand damage. Sahil Agarwal, CEO of Enkrypt AI said, “We are thrilled to announce our strategic partnership with MongoDB, to help companies secure their RAG workflows for faster production deployment. Together, Enkrypt AI and MongoDB are dedicated to delivering unparalleled safety and performance, ensuring that companies can leverage AI technologies with confidence and improved trust.” FriendliAI FriendliAI’s mission is to empower organizations to harness the full potential of their generative AI models with ease and cost efficiency. By eliminating the complexities of generative AI serving, FriendliAI aims to empower more companies to achieve innovation with generative AI. “We’re excited to partner with MongoDB to empower companies in testing and optimizing their RAG features for faster production deployment,” said Byung-Gon Chon, CEO and co-founder of FriendliAI. “MongoDB simplifies the launch of a scalable vector database with operational data. Our collaboration streamlines the entire RAG development lifecycle, accelerating time to market and enabling companies to deliver real value to their customers more swiftly.” HoneyHive HoneyHive helps organizations continuously debug, evaluate, and monitor AI applications, and ship new AI features faster and with confidence. "We’re thrilled to announce our partnership with MongoDB, which addresses a critical challenge in GenAI deployment—the gap between prototyping and production-ready RAG systems,” said Mohak Sharma, CEO of HoneyHive. “By integrating HoneyHive's evaluation and monitoring capabilities with MongoDB's robust vector database, we're enabling developers to build, test, and deploy RAG applications with greater confidence. This collaboration provides the necessary tools for continuous quality assurance, from development through to production. For companies aiming to leverage gen AI responsibly and at scale, our combined solution offers a pragmatic path to faster, more reliable deployment." Iguazio The Iguazio AI platform operationalizes and de-risks ML & gen AI applications at scale so organizations can implement AI effectively and responsibly in live business environments. “We're delighted to expand our partnership with MongoDB into the gen AI domain, jointly helping enterprises build, deploy and manage gen AI applications in live business environments with our gen AI Factory,” said Asaf Somekh, co-founder and CEO of Iguazio (acquired by McKinsey). “Together, we mitigate the challenges of scaling gen AI and minimizing risk with built-in guardrails. Our seamlessly integrated technologies enable enterprises to realize the potential of gen AI and turn their AI strategy into real business impact." Netlify Netlify is the essential platform for the delivery of exceptional and dynamic web experiences, without limitations. The Netlify Composable Web Platform simplifies content orchestration, streamlines and unifies developer workflow, and enables website speed and agility for enterprise teams. "Netlify is excited to join forces with MongoDB to help companies test and optimize their RAG features for faster production deployment,” said Dana Lawson, Chief Technical Officer at Netlify. “MongoDB has made it easy to launch a scalable vector database with operational data, while Netlify enhances the deployment process and speed to production. Our collaboration streamlines the development lifecycle of RAG applications, decreasing time to market and helping companies deliver real value to customers faster." Render Render helps software teams ship products fast and at any scale. The company hosts applications for customers that range from solopreneurs, small agencies, and early stage startups, to mature, scaling businesses with services deployed around the world, all with a relentless commitment to reliability and uptime. Jess Lin, Developer Advocate at Render, said, “We’re thrilled to join forces with MongoDB to help companies effortlessly deploy and scale their applications—from their first user to their billionth. Render and MongoDB Atlas both empower engineers to focus on developing their products, not their infrastructure. Together, we're streamlining how engineers build full-stack apps, which notably include new AI applications that use RAG.” Superlinked Superlinked is a compute framework that helps MongoDB Atlas Vector Search work at the level of documents, rather than individual properties, enabling MongoDB customers to build high-quality RAG, Search, and Recommender systems with ease. “We're thrilled to join forces with MongoDB to help companies build vector search solutions for complex datasets,” said Daniel Svonava, CEO of Superlinked. “MongoDB makes it simple to manage operational data and a scalable vector index in one place. Our collaboration brings the operational data into the vector embeddings themselves, making the joint system able to answer multi-faceted queries like “largest clients with exposure to manufacturing risk” and operate the full vector search development cycle, speeding up time to market and helping companies get real value to customers faster." Twelve Labs Twelve Labs builds AI that perceives the world the way humans do. The company models the world by shipping next-generation multimodal foundation models that push the boundaries in video understanding. "We are excited to partner with MongoDB to enable developers and enterprises to build advanced multimodal video understanding applications,” said Jae Lee, CEO of Twelve Labs. “Developers can store Twelve Labs' state-of-the-art video embeddings in MongoDB Atlas Vector Search for efficient semantic video retrieval—which enables video recommendations, data curation, RAG workflows, and more. Our collaboration supports native video processing and ensures high-performance & low latency for large-scale video datasets." Upstage Upstage specializes in delivering above-human-grade performance AI solutions for enterprises, focusing on superior usability, customizability, and data privacy. “We are thrilled to partner with MongoDB to provide our enterprise customers with a powerful full-stack LLM solution featuring RAG capabilities,” said Sung Kim, CEO and co-founder of Upstage. “By combining Upstage AI's Document AI, Solar LLM, and embedding models with the robust vector database MongoDB Atlas, developers can create a powerful end-to-end RAG application that's grounded with the enterprise's unstructured data. This application achieves a fast time to value with productivity gains while minimizing the risk of hallucination.” But wait, there's more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub , and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem. Head over to our quick-start guide to get started with Atlas Vector Search today.

August 7, 2024

Building Gen AI with MongoDB & AI Partners | June 2024

Even for those of us who work in AI, keeping up with the latest news in the AI space can be head-spinning. In just the last few weeks, OpenAI introduced their newest model (GPT-4o), Anthropic continued to develop Claude with the launch of Claude 3.5 Sonnet, and Mistral launched Mixtral 8x22B, their most efficient open model to date. And those are only a handful of recent releases! In such an ever-changing space, partnerships are critical to combining the strengths of organizations to create solutions that would be challenging to develop independently. Also, it can be overwhelming for any one business to keep track of so much change. So there’s a lot of value in partnering with industry leaders and new players alike to bring the latest innovations to customers. I’ve been at MongoDB for less than a year, but in that time our team has already built dozens of strategic partnerships that are helping companies and developers build AI applications faster and safer. I love to see these collaborations take off! A compelling example is MongoDB’s recent work with Vercel. Our team developed an exciting sample application that allows users to deploy a retrieval-augmented generation (RAG) application on Vercel in just a few minutes. By leveraging a MongoDB URI and an OpenAI key, users can one-click deploy this application on Vercel. Another recent collaboration was with Netlify. Our team also developed a starter template that implements a RAG chatbot on top of their platform using LangChain and MongoDB Atlas Vector Search capabilities for storing and searching the knowledge base that powers the chatbot's responses. These examples demonstrate the power of combining MongoDB's robust database capabilities with other deployment platforms. They also show how quickly and efficiently users can set up fully functional RAG applications, and highlight the significant advantages that partnerships bring to the AI ecosystem. And the best part? We’re just getting started! Stay tuned for more information about the MongoDB AI Applications Program later this month. Welcoming new AI partners Speaking of partnerships, in June we welcomed seven AI partners that offer product integrations with MongoDB. Read on to learn more about each great new partner. AppMap is an open source personal observability platform to help developers keep their software secure, clear, and aligned. Elizabeth Lawler, CEO of AppMap, commented on our joint value for developers. “AppMap is thrilled to join forces with MongoDB to help developers improve and optimize their code. MongoDB is the go-to data store for web and mobile applications, and AppMap makes it easier than ever for developers to migrate their code from other data stores to MongoDB and to keep their code optimized as their applications grow and evolve.” Read more about our partnership and how to use AppMapp to improve the quality of code running with MongoDB. Mendable is a platform that automates customer services providing quick and accurate answers to questions without human intervention. Eric Ciarla, co-founder of Mendable, highlighted the importance of our partnership. "Our partnership with MongoDB is unlocking massive potential in AI applications, from go to market copilots to countless other innovative use cases,” he said. “We're excited to see teams at MongoDB and beyond harnessing our combined technologies to create transformative AI solutions across all kinds of industries and functions." Learn how Mendable and MongoDB Atlas Vector Search power customer service applications. OneAI is an API-first platform built for developers to create and manage trusted GPT chatbots. Amit Ben, CEO of One AI, shared his excitement about the partnership. "We're thrilled to partner with MongoDB to help customers bring trusted GenAI to production. OneAI's platform, with RAG pipelines, LLM-based chatbots, goal-based AI, anti-hallucination guardrails, and language analytics, empowers customers to leverage their language data and engage users even more effectively on top of MongoDB Atlas." Check out some One AI’s GPT agents & advanced RAG pipelines built on MongoDB. Prequel allows companies to sync data to and from their customers' data warehouses, databases, or object storage so they get better data access with less engineering effort. "Sharing MongoDB data just got easier with our partnership,” celebrated Charles Chretien, co-founder of Prequel. “Software companies running on MongoDB can use Prequel to instantly share billions of records with customers on every major data warehouse, database, and object storage service.” Learn how you can share MongoDB data using Prequel. Qarbine complements summary data visualization tools allowing for better informed decision-making across teams. Bill Reynolds, CTO of Qarbine, mentioned the impact of our integration to distill better insights from data: “We’re excited to extend the many MongoDB Atlas benefits upward in the modern application stack to deliver actionable insights from publication quality drill-down analysis. The native integrations enhance in-app real-time decisions, business productivity and operational data ROI, fueling modern application innovation.” Want to power up your insights with MongoDB Atlas and Qarbine? Read more . Temporal is a durable execution platform for building and scaling invincible applications faster. "Organizations of all sizes have built AI applications that are ‘durable by design’ using MongoDB and Temporal. The burden of managing data and agent task orchestration is effortlessly abstracted away by Temporal's development primitives and MongoDB's Atlas Developer Data Platform”, says Jay Sivachelvan, VP of Partnerships at Temporal. He also highlighted the benefits of this partnership. “These two solutions, together, provide compounding benefits by increasing product velocity while also seamlessly automating the complexities of scalability and enterprise-grade resilience." Learn how to build microservices in a more efficient way with MongoDB and Temporal. Unstructured is a platform that connects any type of enterprise data for use with vector databases and any LLM framework. Read more about enhancing your gen AI application accuracy using MongoDB and Unstructured. But wait, there's more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub , and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

July 9, 2024

Building Gen AI with MongoDB & AI Partners: May 2024

Since I joined MongoDB last September, each month has seemed more action-packed than the last. But it’s possible that May was the busiest of all: May 2024 was a month of big milestones for MongoDB! First, we held MongoDB.local NYC on May 2, our biggest .local event so far, with 2,500 attendees from around the world. It was the first MongoDB.local event I attended since joining the company, and suffice it to say I was thrilled to meet with so many colleagues and partners in person. I was particularly excited to discuss the impact of MongoDB Atlas on the generative AI space, since we also announced the new MongoDB AI Applications Program (MAAP) in May. MongoDB’s CEO, Dev Ittycheria, on the MongoDB .Local NYC keynote stage MAAP was launched to help organizations quickly build, integrate, and deploy gen AI-enriched applications at scale. We do this by providing customers a complete package that includes strategic advisory, professional services, and a robust tech stack through MongoDB and our amazing partners: Anthropic, Anyscale, Amazon Web Services (AWS), Cohere, Credal.ai, Fireworks.ai, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI. I really look forward to seeing how MAAP will empower customers to create secure, reliable, and high-performing gen AI applications after the program becomes publicly available in July. Stay tuned for more! And if you’re interested in hearing more about MongoDB’s approach to AI partnerships, and how MAAP will help organizations of all sizes build gen AI applications, check out my interview with theCUBE at MongoDB.local NYC alongside Benny Chen, co-founder of Fireworks.ai. Upcoming AI partner events Are you in San Francisco in late June? We’re proud to sponsor the AI Engineer World’s Fair this year! Stop by the MongoDB booth to chat about gen AI development, and make sure to attend our panel “Building Your AI Stack with MongoDB, Cohere, LlamaIndex, and Together AI” on June 27. Welcoming new AI partners In addition to .local NYC and announcing MAAP in May, we also welcomed four AI partners that offer product integrations with MongoDB: Haystack, Mixpeek, Quotient AI, and Radiant. Read on to learn more about each great new partner. Haystack is an open source Python framework for building custom apps with large language models (LLMs). It allows users to try out the latest models in natural language processing (NLP) while being flexible and easy to use. “We’re excited to partner with MongoDB to help developers build top-tier LLM applications,” said Malte Pietsch, co-founder and CTO of deepset , makers of Haystack and deepset Cloud. “The new Haystack and MongoDB Atlas integration lets developers seamlessly use MongoDB data in Haystack, a reliable framework for creating quality LLM pipelines for use cases like RAG, QA, and agentic pipelines. Whether you're an experienced developer or just starting, your gen AI projects can quickly progress from prototype to adoption, accelerating value for your business and end-users." Learn more about Haystack’s MongoDBAtlasDocumentStore to improve your AI applications. Mixpeek is a multimodal indexing pipeline that gets a database ready for generative AI. It allows developers to treat an object store and a transactional database as a single entity. Ethan Steininger, founder of Mixpeek, explained the value of the MongoDB-Mixpeek integration. “With MongoDB, developers store vectors, metadata, text and all the indexes needed for hyper-targeted retrieval,” he said. “Combined with Mixpeek, they can ensure their S3 buckets and all the documents, images, video, audio and text objects are always consistent with their transactional database, accelerating the path to production by instilling confidence that multimodal RAG results will always be up-to-date." Read more about our partnership and learn how to build real-time multimodal vectors in a MongoDB cluster. Quotient AI is a solution that offers developers the capability to evaluate their AI products with specialized datasets and frameworks to accelerate the experimentation cycle. Julia Neagu, CEO of Quotient AI, highlighted the importance of our partnership. "We are excited to join forces with MongoDB and revolutionize how developers and enterprises are building AI products,” she said. “We share the common goal of helping developers get their ideas to market faster with a first-class developer experience. MongoDB Atlas scalable and versatile vector database technology complements Quotient's mission to ship high-quality, reliable AI applications through rapid, domain-specific evaluation." Learn more how Quotient AI enables evaluation and refinement of RAG-powered AI products built on MongoDB Atlas. Radiant offers a monitoring and evaluation framework for production AI use cases. Nitish Kulnani, CEO of Radiant, shared his excitement about the partnership with MongoDB to enhance the reliability of AI applications. “By combining Radiant's anomaly detection with MongoDB Atlas Vector Search, we enable developers to swiftly identify and mitigate risks, and quickly deploy high-quality AI solutions, delivering real value to customers faster,” he said. “MongoDB trusts Radiant to accelerate its own AI applications, and we're excited to deliver the same experience to MongoDB customers.'' Read more about how to deploy Radiant with MongoDB Atlas to accelerate your journey from development to production. But wait, there’s more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub , and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

June 5, 2024

Collaborating to Build AI Apps: MongoDB and Partners at Google Cloud Next '24

From April 9 to April 11, Las Vegas became the center of the tech world, as Google Cloud Next '24 took over the Mandalay Bay Convention Center—and the convention’s spotlight shined brightest on gen AI. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Between MongoDB’s big announcements with Google Cloud (which included an expanded collaboration to enhance building, scaling, and deploying GenAI applications using MongoDB Atlas Vector Search and Vertex AI ), industry sessions, and customer meetings, we offered in-booth lightning talks with leaders from four MongoDB partners—LangChain, LlamaIndex, Patronus AI, and Unstructured—who shared valuable insights and best practices with developers who want to embed AI into their existing applications or build new-generation apps powered by AI. Developing next-generation AI applications involves several challenges, including handling complex data sources, incorporating structured and unstructured data, and mitigating scalability and performance issues in processing and analyzing them. The lightning talks at Google Cloud Next ‘24 addressed some of these critical topics and presented practical solutions. One of the most popular sessions was from Harrison Chase , co-founder and CEO at LangChain , an open-source framework for building applications based on large language models (LLMs). Harrison provided tips on fixing your retrieval-augmented generation (RAG) pipeline when it fails, addressing the most common pitfalls of fact retrieval, non-semantic components, conflicting information, and other failure modes. Harrison recommended developers use LangChain templates for MongoDB Atlas to deploy RAG applications quickly. Meanwhile, LlamaIndex —an orchestration framework that integrates private and public data for building applications using LLMs—was represented by Simon Suo , co-founder and CTO, who discussed the complexities of advanced document RAG and the importance of using good data to perform better retrieval and parsing. He also highlighted MongoDB’s partnership with LlamaIndex, allowing for ingesting data into the MongoDB Atlas Vector database and retrieving the index from MongoDB Atlas via LlamaParse and LlamaCloud . Guillaume Nozière - Patronus AI Andrew Zane - Unstructured Amidst so many booths, activities, and competing programming, a range of developers from across industries showed up to these insightful sessions, where they could engage with experts, ask questions, and network in a casual setting. They also learned how our AI partners and MongoDB work together to offer complementary solutions to create a seamless gen AI development experience. We are grateful for LangChain, LlamaIndex, Patronus AI, and Unstructured's ongoing partnership. We look forward to expanding our collaboration to help our joint customers build the next generation of AI applications. To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with these and other AI partners.

April 23, 2024

Collaboration for Breakfast: MongoDB and Partners Share AI Insights at AWS re:Invent

I’m old enough to remember when every tech conversation didn’t include the term “AI.” Hardly a day goes by without some mention of AI or generative AI (gen AI). But don’t just take my word for it: Google News search results for the phrase, “generative AI” have grown more than 2000% since ChatGPT was launched in November 2022. The AI excitement is more than just hype. For example, we’re seeing widespread adoption of AI across MongoDB’s tens of thousands of customers. Meanwhile, a recent GitHub survey showed 92% of developers have already incorporated gen AI into their work, and Gartner predicts that by 2027, 90% of new applications will incorporate machine learning models or services. MongoDB and our partners tapped into this excitement during AWS re:Invent. On November 29 — the same morning the company announced the integration of MongoDB Atlas Vector Search and Amazon Bedrock (and less than a week before Atlas Vector Search was made generally available ) — MongoDB held an AI-themed breakfast that reinforced the importance of partnerships during this transformative time. During the breakfast, MongoDB product leaders sat down with leaders from four of the company’s partners — Gradient, LangChain, Nomic, and Unstructured—to share insights about building the next generation of AI applications. Despite its 7 a.m. start time, the breakfast was packed with attendees from a range of industries and geographies — no small feat given re:Invent’s busy schedule — and excitement for AI was palpable. Given the broad interest in all things generative AI, organizations of all sizes want to learn how they can build the applications of tomorrow. This is where MongoDB and partners come in: MongoDB provides an integrated developer data platform that accelerates innovation by simplifying the application development process. To streamline AI innovation, MongoDB partners with organizations that offer complementary technology solutions, interoperability, flexibility, and reliability. Partnering to deliver a complete AI toolkit For example, Unstructured works with MongoDB to help organizations connect enterprise data stored in difficult formats like PDF and PNG to AI models. And the combination of MongoDB and LangChain's application framework makes it possible to build solutions that leverage proprietary company data. Meanwhile, with MongoDB Atlas Vector Search and Gradient , organizations can build, customize, and run private AI applications that leverage industry expert large language models (LLMs) to enhance performance. And last but hardly least, Nomic's tools allow users to visualize the unstructured data they store in MongoDB, to make AI more explainable and accessible. All told each partner’s offerings work with MongoDB products to create a comprehensive set of tools with which developers can build AI applications. At the breakfast, company leaders shared their thoughts on the current AI landscape, how their organizations collaborate with MongoDB, and what they see as the future of AI tools. “At AWS re:Invent, we showed how MongoDB is the best platform for building enterprise-ready generative AI apps,” said Andrew Davidson, senior vice president of product management at MongoDB. “Our powerful developer data platform — which works seamlessly with cutting-edge AI ecosystem partners to enable openly composable architecture and design — empowers developers to create compelling AI apps and experiences with greater interoperability, simplification, flexibility, and choice, pushing the boundaries of what's possible.” For example, LangChain Founding Software Engineer Jacob Lee noted that “it’s so, so early for generative AI. Most attendees at re:Invent had only just begun to consider principles and use cases for the technology. There is so much opportunity and potential impact yet to emerge that it will truly take the entire ecosystem's talents and creativity to explore it all.” “In short, the most important thing is to support each other and just keep building cool things,” said Lee. Brian Raymond, founder and CEO of Unstructured, agreed that it's very early for generative AI. "We should start seeing incremental, yet exciting, gains in the performance of multimodal foundation models as well as increased focus on smaller models that are cheaper to run at scale," Raymond added. "It's likely going to take more time to mature the emerging foundation model stack (marked by retrieval-augmented generation ) into a performant and cost-effective option for most organizations." Creating a seamless AI development experience Overall, the re:Invent breakfast conversation conversation highlighted how MongoDB and its partners are working together to create a holistic, seamless AI development experience. By working closely with partner organizations to augment its industry-leading solutions , MongoDB ensures enterprises have access to everything they need in one place to develop cutting-edge, modern AI applications that are scalable, secure, and enterprise-grade. “Gradient's mission is to democratize AI by making it more accessible to enterprises and developers,” said Chris Chang, CEO and co-founder of Gradient. “However in AI, data itself can be challenging which is why our partnership with MongoDB will allow users to make the most out of their data and leverage a best-of-breed technology to help power new AI features.” To learn more about MongoDB’s artificial intelligence solutions—including resources to build next-generation applications — visit MongoDB for Artificial Intelligence . If your organization wants to build the next big thing in AI with MongoDB, consider applying for the MongoDB AI Innovators Program .

January 18, 2024