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

Gregory Maxson
January 18, 2024

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.

← Previous

Navigating the Landscape of Artificial Intelligence: How Can The Financial Sector Make Use of Generative AI

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . In the ever-evolving landscape of financial technology, the conversation around artificial intelligence (AI), and particularly generative AI is gaining momentum. AI has been part of the financial landscape for decades but with the advances in generative AI come greater benefits but also risks that financial institutions need to consider in such a regulated industry. While the potential benefits of generative AI are significant and the adoption by many is still being considered, a measured approach is needed when moving from proof of concept into production. In an edition of the Fintech Finance News Virtual Arena , several notable industry thought leaders from HSBC, Capgemini, and MongoDB came together to explore how the financial sector can make use of generative AI and what financial institutions must consider in their AI strategy. Watch the panel discussion How can the financial sector make use of generative AI today with HSBC, MongoDB and Capgemini . Hear from: EJ Achtner, Office of Applied Artificial Intelligence at HSBC Dan Pears, Vice President, UK Practice Lead at Capgemini Wei You Pan, Director, Financial Services Industry Solutions at MongoDB Doug Mackenzie, Chief Content Officer at FF News Addressing the challenges of generative AI While financial technologists have always had to deal with persistent issues like risk management and governance, the adoption of generative AI in fintech introduces new challenges that AI specialists have always dealt with, like inherent biases and ethical concerns. One challenge that stands out for generative AI is hallucination — the generation of content that is not accurate, factual, or reflective of the real world. AI models may produce information that sounds plausible but is entirely fictional. Generative AI models, especially in natural language processing, might generate text that is coherent and contextually appropriate but lacks factual accuracy. This poses challenges in different domains, including misinformation and content reliability. Examples of such challenges or risks may include: Misleading financial planning advice: In financial advisory services, hallucinated information may result in misleading advice leading to unexpected risks or missed opportunities. Incorrect risk assessments for lending: Inaccurate risk profiles may lead to poor risk assessments for loan applicants that can cause a financial institution to approve a loan at a higher risk of default than the firm would normally accept. Sensitive information in generated text: When generating text, models may inadvertently include sensitive information from the training data. Adversaries can craft input prompts to coax the model into generating outputs that expose confidential details present in the training corpus. It is thus paramount that financial institutions understand the technological impact, scale, and complexity associated with AI, especially the generative AI strategy. A strategic and comprehensive approach that encompasses various aspects of technology, data, ethics, and organizational readiness is critical. Here are some key considerations financial institutions must consider when adopting such a strategy: Hallucination mitigation: Mitigating hallucination in generative AI is a challenging task, but several strategies and techniques can be employed to reduce the risk of generating inaccurate or misleading information. One promising strategy is to make use of the Retrieval Augmented Generation (RAG) approach to mitigate hallucination in generative AI models. This approach involves incorporating information retrieval mechanisms to enhance the generation process, ensuring that generated content is grounded in real-world knowledge. Vector Search is a popular mechanism to support the implementation of the RAG architecture which uses vector search to retrieve relevant documents based on the input query. It then provides these retrieved documents as context to the large language models (LLM) to help generate a more informed and accurate response. Data quality and availability: Take a step back before adopting AI to ensure the quality, relevance, and accuracy of data being used for AI training and decision-making can be accessed in real time. Education: Investing in training programs is key to addressing the skills gap in AI, ensuring the workforce is equipped to manage, interpret, and collaborate with AI technologies. For the adoption of AI to be successful, a culture of learning and development is vital, providing employees with the tools needed to be the absolute best that they can be for their personal and professional development. Furthermore, promoting awareness about potential vulnerabilities and continuously refining models to enhance their resilience against hallucination, biases, adversarial manipulation, and other weaknesses are essential to ensure success in generative AI applications. Develop new governance, frameworks, and controls: Before going live, create safe and secure environments for testing and learning that allow you to fail fast in a safe manner. Moving headfirst into production with direct contact with customers can result in the wrong governance methods being implemented. Monitoring and continuous improvement: Implement robust monitoring systems to measure and understand financial impacts, change impacts, scale, and complexity associated with the adoption of AI. Scalability and integration: Design AI systems with scalability in mind to accommodate growing datasets and evolving requirements. Security and privacy: Implement robust cybersecurity measures to safeguard AI models and the data they rely on. Techniques such as adversarial training, input sanitization, and incorporating privacy-preserving mechanisms can help mitigate the risk of generative AI inadvertently revealing private data. Incident response plans should be part of the cybersecurity measures, as well as regular education of the relevant stakeholders on security and privacy. How MongoDB can help you overcome your data challenges In the realm of adopting advanced technologies like AI and ML which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems, particularly when it comes to use cases such as fraud prevention where the platform is integrated with external sources for accurate data analysis on complete data. The inflexibility of existing systems poses a significant pain point, hindering the seamless incorporation of cutting-edge technologies. MongoDB serving as the operational data store (ODS) with a flexible document model enables financial institutions to efficiently handle large volumes of data in real time. By integrating MongoDB with AI/ML platforms, businesses can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. Legacy systems, marked by their inflexibility and resistance to modification present another challenge in the pursuit of leveraging AI to enhance customer experiences and improve operational efficiency. Integration struggles also persist, especially in the financial sector, where the uncertainty of evolving AI models over time requires a scalable infrastructure. MongoDB's developer data platform future-proofs businesses with its flexible data schema capable of accommodating any data structure, format, or source. This flexibility facilitates seamless integration with different AI/ML platforms, allowing financial institutions to adapt to changes in the AI landscape without extensive modifications to the infrastructure. Concerns regarding the security of customer data, especially when shared with third parties through APIs, further complicate the adoption of innovative AI technologies. Legacy systems can stand in the way of innovation as they are often more vulnerable to security threats due to outdated security measures. MongoDB’s modern developer data platform addresses these challenges with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas , a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to discover more about building AI-enriched applications with MongoDB, take a look at the following resources: Mitigate hallucination of Generative AI by using RAG with Atlas Vector Search, LangChain, and OpenAI Deliver AI-enriched apps with the right security controls in place, and at the scale and performance users expect Sign up for our Atlas for Industries programme to get access to our solution accelerators to drive innovation

January 18, 2024
Next →

Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024