Artificial Intelligence
Building AI-powered Apps with MongoDB
Elevate Your Java Applications with MongoDB and Spring AI
MongoDB is excited to announce an integration with Spring AI, enhancing MongoDB Atlas Vector Search for Java developers. This collaboration brings Vector Search to Java applications, making it easier to build intelligent, high-performance AI applications. Why Spring AI? Spring AI is an AI library designed specifically for Java, applying the familiar principles of the Spring ecosystem to AI development. It enables developers to build, train, and deploy AI models efficiently within their Java applications. Spring AI addresses the gap left by other AI frameworks and integrations that focus on other programming languages, such as Python, providing a streamlined solution for Java developers. Spring has been a cornerstone for Java developers for decades, offering a consistent and reliable framework for building robust applications. The introduction of Spring AI continues this legacy, providing a straightforward path for Java developers to incorporate AI into their projects. With the MongoDB-Spring integration, developers can leverage their existing Spring knowledge to build next-generation AI applications without the friction associated with learning a new framework. Key features of Spring AI include: Familiarity: Leverage the design principles of the Spring ecosystem. Spring AI allows Java developers to use the same familiar tools and patterns they already know from other Spring projects, reducing the learning curve and allowing them to focus on building innovative AI applications. This means you can integrate AI capabilities—including Atlas Vector Search—without having to learn a new language or framework, making the transition smoother and more intuitive. Portability: Applications built with Spring AI can run anywhere the Spring framework runs. This ensures that AI applications are highly portable and can be deployed across various environments without modification, guaranteeing flexibility and consistency in deployment strategies. Modular design: Use Plain Old Java Objects (POJOs) as building blocks. Spring AI’s modular design promotes clean code architecture and maintainability. By using POJOs, developers can create modular, reusable components that simplify the development and maintenance of AI applications. This modularity also facilitates easier testing and debugging, leading to more robust applications that efficiently integrate with Atlas Vector Search. Efficiency: Streamline development with tools and features designed for AI applications in Java. Spring AI provides a range of tools that enhance development efficiency, including pre-built templates, configuration management, and integrated testing tools. These features reduce the time and effort required to develop AI applications, allowing developers to bring their ideas to market faster. These features streamline AI development by enhancing the integration and performance of Atlas Vector Search within Java applications, making it easier to build and scale AI-driven features. Enhancing AI development with Spring AI and Atlas Vector Search MongoDB Atlas Vector Search enhances AI application development by providing advanced search capabilities. The new Spring AI integration enables developers to manage and search vector data within AI models, enabling features like recommendation systems, natural language processing, and predictive analytics. Atlas Vector Search allows you to store, index, and search high-dimensional vectors, which are crucial for AI and machine learning models. This capability supports a range of AI features: Recommendation systems: Provide personalized recommendations based on user behavior and preferences. Natural language processing: Enhance text analysis and understanding for chatbots, sentiment analysis, and more. Predictive analytics: Improve forecasting and decision-making with advanced data models. What the integration means for Java developers Prior to MongoDB-Spring integration, Java developers did not have an easy way to integrate Spring into their AI applications using MongoDB Atlas Vector Search, which led to longer development times and suboptimal application performance. With this integration, the Java development landscape is transformed, allowing developers to build and deploy AI applications with greater efficiency. The integration simplifies the entire process, enabling developers to concentrate on creating innovative solutions rather than dealing with integration hurdles. This approach not only reduces development time but also accelerates time-to-market. Additionally, MongoDB offers robust support through comprehensive tutorials and a wealth of community-driven content. Whether you’re just beginning or looking to optimize existing applications, you’ll find the resources and guidance you need at every stage of your development journey. Get started! The MongoDB and Spring AI integration is designed to simplify the development of intelligent Java applications. By combining MongoDB's robust data platform with Spring AI's capabilities, you can create high-performance applications more efficiently. To start using MongoDB with Spring AI, explore our documentation , tutorial , and check out our GitHub repository to build the next generation of AI-driven applications today.
Better Business Loans with MongoDB and Generative AI
Business loans are a cornerstone of banking operations, providing significant benefits to both financial institutions and broader economies. For example, in 2023 the value of commercial and industrial loans in the United States reached nearly $2.8 trillion . However, these loans can present unique challenges and risks that banks must navigate. Besides credit risk, where the borrower may default, banks also face business risk, in which economic downturns or sector-specific declines can impact borrowers' ability to repay loans. In this post, we dive into the potential of generative AI to generate detailed risk assessments for business loans, and how MongoDB’s multimodal features can be leveraged for comprehensive and multidimensional risk analyses. The critical business plan A business plan is essential for a business loan as it serves as a comprehensive roadmap detailing the borrower's plans, strategies, and financial projections. It helps lenders understand the business's goals, viability, and profitability, demonstrating how the loan will be used for growth and repayment. A detailed business plan includes market analysis, competitive positioning, operational plans, and financial forecasts which build a compelling case for the lender's investment and the business’s ability to manage risks effectively, increasing the likelihood of securing the loan. Reading through borrower credit information and detailed business plans (roughly 15-20 pages long ) poses significant challenges for loan officers due to time constraints, the material’s complexity, and the difficulty of extracting key metrics from detailed financial projections, market analyses, and risk factors. Navigating technical details and industry-specific jargon can also be challenging and require specialized knowledge. Identifying critical risk factors and mitigation strategies only adds further complexity along with ensuring accuracy and consistency among loan officers and approval committees. To overcome these challenges, gen AI can assist loan officers by efficiently analyzing business plans, extracting essential information, identifying key risks, and providing consistent interpretations, thereby facilitating informed decision-making. Assessing loans with gen AI Interactive risk analysis with gen AI-powered chatbots Gen AI can help analyze business plans when built on a flexible developer data platform like MongoDB Atlas . One approach is implementing a gen AI-powered chatbot that allows loan officers to "discuss" the business plan. The chatbot can analyze the input and provide insights on the various risks associated with lending to the borrower for the proposed business. MongoDB sits at the heart of many customer support applications due to its flexible data model that makes it easy to build a single, 360-degree view of data from a myriad of siloed backend source systems. Figure 1 below shows an example of how ChatGPT-4o responds when asked to assess the risk of a business loan. Although the input of the loan purpose and business description is simplistic, gen AI can offer a detailed analysis. Figure 1: Example of how ChatGPT-4o could respond when asked to assess the risk of a business loan Hallucinations or ignorance? By applying gen AI to risk assessments, lenders can explore additional risk factors that gen AI can evaluate. One factor could be the risk of natural disasters or broader climate risks. In Figure 2 below, we added flood risk specifically as a factor to the previous question to see what the ChatGPT4-o comes back with. Figure 2: Example of how ChatGPT-4o responded to flood risk as a factor Based on the above, there is a low risk of flooding. To validate this, we asked ChatGPT-4o the question differently, focusing on its knowledge of flood data. It suggested reviewing FEMA flood maps and local flood history, indicating it might not have the latest information. Figure 3: Asking location-specific flood questions In the query shown in Figure 3 above, ChatGPT gave an opposite answer and indicated there is “significant flooding” providing references to flood evidence after having performed an internet search across 4 sites which it did not perform previously. From this example, we can see that when ChatGPT does not have the relevant data, it starts to make false claims, which can be considered hallucinations. Initially, it indicated a low flood risk due to a lack of information. However, when specifically asked about flood risk in the second query, it suggested reviewing external sources like FEMA flood maps, recognizing its limitations and need for external validation. Gen AI-powered chatbots can recognize and intelligently seek additional data sources to fill their knowledge gaps. However, a causal web search won’t provide the level of detail required. Retrieval-augmented generation-assisted risk analysis The promising example above demonstrates the experience of how gen AI can augment loan officers to analyze business loans. However, interacting with a gen AI chatbot relies on loan officers repeatedly prompting and augmenting the context with relevant information. This can be time-consuming and impractical due to the lack of prompt engineering skills or the lack of data needed. Below is a simplified solution of how gen AI can be used to augment the risk analysis process to fill the knowledge gap of the LLM. This demo uses MongoDB as an operational data store leveraging geospatial queries to find out the floods within 5km of the proposed business location. The prompting for this risk analysis highlights the analysis of the flood risk assessment rather than the financial projections. A similar test was performed on Llama 3 , hosted by our MAAP partner Fireworks.AI . It tested the model’s knowledge of flood data showing a similar knowledge gap as ChatGPT-4o. Interestingly, rather than providing misleading answers, LLama 3 provided a “hallucinated list of flood data,” but highlighted that “this data is fictional and for demonstration purposes only. In reality, you would need to access reliable sources such as FEMA's flood data or other government agencies' reports to obtain accurate information.” Figure 4: LLM’s response with Fictional flood locations With this consistent demonstration of the knowledge gap in the LLMs in specialized areas, it reinforces the need to explore how RAG (retrieval-augmented generation) with a multimodal data platform can help. In this simplified demo, you select a business location, a business purpose, and a description of a business plan. To make inputs easier, an “Example” button has been added to leverage gen AI to generate a sample brief business description to avoid the need to key in the description template from scratch. Figure 5: Choosing a location on the map and writing a brief plan description Upon submission, it will provide an analysis using RAG with the appropriate prompt engineering to provide a simplified analysis of the business with the consideration of the location and also the flood risk earlier downloaded from external flood data sources. Figure 6: Loan risk response using RAG In the Flood Risk Assessment section, gen AI-powered geospatial analytics enable loan officers to quickly understand historical flood occurrences and identify the data sources. You can also reveal all the sample flood locations within the vicinity of the business location selected by clicking on the “Pin” icon. The geolocation pins include the flood location and the blue circle indicates the 5km radius in which flood data is queried, using a simple geospatial command $geoNear . Figure 7: Flood locations displayed with pins The following diagram provides a logical architecture overview of the RAG data process implemented in this solution highlighting the different technologies used including MongoDB, Meta Llama 3, and Fireworks.AI. Figure 8: RAG data flow architecture diagram With MongoDB's multimodal capabilities, developers can enhance the RAG process by utilizing features such as network graphs, time series, and vector search. This enriches the context for the gen AI agent, enabling it to provide more comprehensive and multidimensional risk analysis through multimodal analytics. Building risk assessments with MongoDB When combined with RAG and a multimodal developer data platform like MongoDB Atlas , gen AI applications can provide more accurate and context-aware insights to reduce hallucination and offer profound insights to augment a complex business loan risk assessment process. Due to the iterative nature of the RAG process, the gen AI model will continually learn and improve from new data and feedback, leading to increasingly accurate risk assessments and minimizing hallucinations. A multimodal data platform would allow you to fully maximize the capabilities of the multimodal AI models. If you would like to discover how MongoDB can help you on this multimodal gen AI application journey, we encourage you to apply for an exclusive innovation workshop with MongoDB's industry experts to explore bespoke modern app development and tailored solutions to your organization. Additionally, you can enjoy these resources: Solution GitHub: Loan Risk Assessor How Leading Industries are Transforming with AI and MongoDB Atlas Accelerate Your AI Journey with MongoDB’s AI Applications Program The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects
Find Hidden Insights in Vector Databases: Semantic Clustering
Vector databases, a powerful class of databases designed to optimize the storage, processing, and retrieval of large volume, multi-dimensional data, have increasingly been instrumental to generative AI (gen AI) applications, with Forrester predicted a 200% increase in the adoption of vector databases in 2024. But their power extends far beyond these applications. Semantic vector clustering, a technique within vector databases, can unlock hidden knowledge within your organization’s data, democratizing insights across teams. Mining diverse data for hidden knowledge Imagine your organization’s data as a library of diverse knowledge—a treasure trove of information waiting to be unearthed. Traditionally, uncovering valuable insights from data often relied on asking the right questions, which can be a challenge for developers, data scientists, and business leaders alike. They might spend vast amounts of time sifting through limited, siloed datasets, potentially missing hidden gems buried within the organization's vast data troves. Simply put, without knowing the right questions to ask, these valuable insights often remain undiscovered, leading to missed opportunities or losses. Enter vector databases and semantic vector clustering. A vector database is designed to store and manage unstructured data efficiently. Within a vector database, semantic vector clustering is a technique for organizing information by grouping vectors with similar meaning together. Text analysis, sentiment analysis, knowledge classification, and uncovering semantic connections between data sets—these are just a few examples of how semantic vector clustering empowers organizations to vastly improve data mining. Semantic vector clustering offers a multifaceted approach to organizational improvement. By analyzing text data, it can illuminate customer and employee sentiments, behaviors, and preferences, informing strategic decisions, enhancing customer service, and optimizing employee satisfaction. Furthermore, it revolutionizes knowledge management by categorizing information into easily accessible clusters, thereby boosting collaboration and efficiency. Finally, by bridging data silos and uncovering hidden relationships, semantic vector clustering facilitates informed decision-making and breaks down organizational barriers. For example, the business can gain significant insights from its customer interaction data which is routinely kept, classified, or summarized. Those data points (texts, numbers, images, videos, etc.) can be vectorized and semantic vector clustering applied to identify the most prominent customer patterns (the densest vector clusters) from those interactions, classifications, or summaries. From the identified patterns, the business can take further actions or make more informed decisions that they wouldn’t have been able to do otherwise. The power of semantic vector clustering So, how does semantic vector clustering achieve all this? Discover semantic structures: Clustering groups similar LLM-embedded vector sets together. This allows for fast retrieval of themes. Beyond clustering regular vectors (individual data points or concepts), clustering RAG vectors (summarization of themes and concepts) can provide superior LLM contexts compared to basic semantic search. Reduce data complexity via clustering: Data points are grouped based on overall similarity, effectively reducing the complexity of the data. This reveals patterns and summarizes key features, making it easier to grasp the bigger picture. Imagine organizing the library by theme or genre, making it easier to navigate vast amounts of information. Semantic auto-aggregation: Here is the coolest part. We can classify groups of vectors into hierarchies by effectively semantically "auto-aggregating" them. This means that the data itself “figures out” these groups and "self-organizes." Imagine a library with an efficient automated catalog system, allowing researchers to find what they need quickly and easily. Vector clustering can be used to create hierarchies, essentially "auto-aggregating" groups of vectors semantically. Think of it as automatically organizing sections of the library based on thematic connections without a set of pre-built questions. This allows you to identify patterns within a vast, semantically-diverse data within your organization. Unlock hidden insights in your vector database The semantic clustering of vector embeddings is a powerful tool to go beyond the surface of data and identify meanings that otherwise would not have been discovered. By unlocking hidden relationships and patterns, you can extract valuable insights that drive better decision-making, enhance customer experiences, and improve overall business efficiency—all enabled through MongoDB’ secure, unified, and fully-managed vector database capabilities. Head over to our quick-start guide to get started with Atlas Vector Search today. Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.
MongoDB AI Course in Partnership with Andrew Ng and DeepLearning.AI
MongoDB is committed to empowering developers and meeting them where they are. With a thriving community of 7 million developers across 117 regions, MongoDB has become a cornerstone in the world of database technology. Building on this foundation, we're excited to announce our collaboration with AI pioneer Andrew Ng and DeepLearning.AI, a leading educational technology company specializing in AI and machine learning. Together, we've created an informative course that bridges the gap between database technology and modern AI applications, further enhancing our mission to support developers in their journey to build innovative solutions. Introducing "Prompt Compression and Query Optimization" MongoDB’s latest course on DeepLearning.AI, Prompt Compression and Query Optimization , covers the prominent form factor of modern AI applications today: Retrieval Augmented Generation (RAG) . This course showcases how MongoDB Atlas Vector Search capabilities enable developers to build sophisticated AI applications, leveraging MongoDB as an operational and vector database. To ensure that learners taking this course are not just introduced to vector search, the course presents an approach to reducing the operational cost of running AI applications in production by a technique known as prompt compression. “RAG, or retrieval augmented generation, has moved from being an interesting new idea a few months ago to becoming a mainstream large-scale application.” — Andrew Ng, DeepLearning.AI Key course highlights RAG Applications: Learn to build and optimize the most prominent form of AI applications using MongoDB Atlas and the MongoDB Query Language(MQL). MongoDB Atlas Vector Search: Leverage the power of vector search for efficient information retrieval. MongoDB Document Model: Explore MongoDB's flexible, JSON-like document model, which represents complex data structures and is ideal for storing and querying diverse AI-related data. Prompt Compression: Use techniques to reduce the operational costs of AI applications in production environments. In this course, you'll learn techniques to enhance your RAG applications' efficiency, search relevance, and cost-effectiveness. As AI applications become more sophisticated, efficient data retrieval and processing becomes crucial. This course bridges the gap between traditional database operations and modern vector search capabilities, enabling you to confidently build robust, scalable AI applications that can handle real-world challenges. MongoDB's document model: The perfect fit for AI A key aspect of this course is that it introduces learners to MongoDB's document model and its numerous benefits for AI applications: Python-Compatible Structure: MongoDB's BSON format aligns seamlessly with Python dictionaries, enabling effortless data representation and manipulation. Schema Flexibility: Adapt to varied data structures without predefined schemas, matching the dynamic nature of AI applications. Nested Data Structures: Easily represent complex, hierarchical data often found in AI models and datasets. Efficient Data Ingestion: Directly ingest data without complex transformations, speeding up the data preparation process. Leveraging the combined insights from MongoDB and DeepLearning.AI, this course offers a perfect blend of practical database knowledge and advanced AI concepts. Who should enroll? This course is ideal for developers who: Are familiar with vector search concepts Building RAG applications and Agentic Systems Have a basic understanding of Python and MongoDB and are curious about AI Want to optimize their RAG applications for better performance and cost-efficiency This course offers an opportunity to grasp techniques in AI application development. You'll gain the skills to build more efficient, powerful, cost-effective RAG applications, from advanced query optimization to innovative prompt compression. With hands-on code, detailed walkthroughs, and real-world applications, you'll be equipped to tackle complex AI challenges using MongoDB's robust features. Take advantage of this chance to stay ahead in the rapidly evolving field of AI. Whether you're a seasoned developer or just starting your AI journey, this course will provide invaluable insights and practical skills to enhance your capabilities. Improve your AI application development skills with MongoDB's practical course. Learn to build efficient RAG applications using vector search and prompt compression. Enroll now and enhance your developer toolkit.
How MongoDB Scales CoPilot AI’s Humanized Sales Interactions
In a world where sales and marketing are the engines behind many tech companies’ growth in a highly competitive landscape, it’s more important than ever that those functions find better and fresher ways to implement personalization into campaigns, sales pitches, and everything in between to reach more customers. CoPilot AI has been at the helm of helping businesses do just that through their AI-powered sales enablement tool, automating personalized interactions to achieve revenue growth, all with the help of MongoDB . CoPilot AI is a software company that helps businesses leverage AI to personalize and automate sales outreach. “We’re looking to humanize digital interactions in a scalable way. That’s our mission, our ethos behind our entire business, which can sometimes seem counterintuitive to people when you think of ‘AI’ and ‘humanize’,” said Scott Morgan, Head of Product Marketing at CoPilot AI. They integrate with platforms that have a high-quality lead base or verified accounts to identify qualified leads and facilitate communication through features like smart replies and sentiment analysis. Today, they predominantly work with LinkedIn as a channel, tapping into the 1B+ professionals globally who interact and conduct business online. “We envision ourselves as having an AI suite of assisting tools that allow business professionals and companies to support their entire sales journey with AI tooling,” said Morgan. CoPilot AI uses five AI pieces (sentiment analysis, reply prediction, smart reply, Meetings Booked AI, and personalized insights) to qualify leads within its lead management platform. Reply prediction gives predictions on which leads are most likely to book meetings with you before you connect with them, while sentiment analysis analyzes replies from leads to determine if they’re interested in continuing the conversation. These features prioritize high-quality leads, boosting success rates. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The challenge: Scaling AI-powered sales interactions CoPilot AI leverages Random Forest and Chat GPT-3.5 turbo for production and Chat GPT-4 for producing labels and creating models. Their tech stack also includes AWS SageMaker and Azure. However, efficiently managing the data behind these interactions was crucial for scaling their platform. They needed a powerful database to hold and manage their massive system records. Also, they needed a scalable and cost-effective platform that could accommodate their data needs for their growing user bases. Enter, MongoDB Atlas. For most of its 10-year journey, CoPilot AI has been using MongoDB. When evaluating alternative cloud database solutions, CoPilot AI explored Microsoft’s Azure Cosmos DB. While Azure Cosmos DB offered a compelling feature set, its pricing structure didn’t align with CoPilot AI’s specific data access patterns, resulting in high costs. This led them to MongoDB for optimal cost-efficiency for their workload. Building a scalable foundation with MongoDB Atlas CoPilot AI has been using MongoDB since 2013 and started using MongoDB Atlas in 2020. “Everything! System of record, campaigns, message sequences, sending messages, all of that is in MongoDB,” says Takahiro Kato, Principal Engineer at CoPilot AI. CoPilot AI also uses Atlas Data Federation to access its customer information, leads, and campaign conversations. They set up data lake pipelines that go into Data Federation, where their ML engineers pull the data from. They also use Online Archive quite extensively. As a fast-growing startup, CoPilot AI was also able to take advantage of the MongoDB for Startups program , giving them access to free credits and expert technical advice to optimize their usage. “Access to the consultant was quite useful as well. We received advice on how to improve query efficiency, something we’ve been struggling with for a while. In the past, the cost was quite high, our queries were inefficient. As we were going through and fixing those issues, the advice helped,” says Kato. MongoDB empowered CoPilot AI with streamlined development through an intuitive driver and data flexibility via its schema-less design, enabling developers to focus on core functionalities while effortlessly adapting the data model for business growth. CoPilot AI continues to use MongoDB Atlas for multiple reasons, some of which include: Speed and Performance: MongoDB's fast read/write capabilities ensure smooth operation for CoPilot AI's data-intensive operations. Developer Productivity: The C# driver with LINQ support simplifies data access for CoPilot AI's .NET backend, boosting development efficiency. Scalability: MongoDB's flexible schema easily accommodates CoPilot AI's evolving data needs as its user base grows. Cost Optimization: Compared to alternatives, MongoDB offered a more cost-effective option for CoPilot AI's data storage needs. Plus, the MongoDB for Atlas Startups Program provided valuable credits and expert guidance to optimize queries and reduce costs. Key takeaways for developers and businesses MongoDB Atlas securely unifies operational, unstructured, and AI-related data to streamline building AI-enriched applications. Considering leveraging AI in your business? Look no further than MongoDB as your database management solution. If you want to learn more about how you can get started with your next AI project or take your AI capabilities to the next level, you can check out our MongoDB for Artificial Intelligence resources page for the latest best practices that get you started in turning your idea into an AI-driven reality. Also, 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. Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.
Enhancing Retail with Retrieval-Augmented Generation (RAG)
In the rapidly evolving retail landscape, tech innovations are reshaping how businesses operate and interact with customers. Generative AI could add up to $275 billion of profit to the apparel, fashion, and luxury sectors’ by 2028, according to McKinsey analysis . One of the most promising developments in this realm is retrieval-augmented generation (RAG) , a powerful application of artificial intelligence (AI) that combines the strength of data retrieval with generative capabilities to supercharge retail enterprises. RAG offers compelling advantages specifically tailored for retailers looking to enhance their operations and customer engagement from personalization to enhanced efficiency. Let’s delve into how RAG is revolutionizing the retail sector. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Why RAG in retail Imagine a customer walks into your store, and based on their previous opt-in online interactions, your technology recognizes their preferences and seamlessly guides them through a personalized service—a feat made possible by RAG. Central to RAG’s effectiveness is its ability to integrate and analyze diverse data sources scattered across data warehouses. This integration enables retailers to gain comprehensive insights into their business performance, understand consumer behavior patterns, and make data-driven decisions swiftly. Below are some of the compelling advantages that RAG can offer: Personalization: RAG enables retailers to deliver highly personalized customer experiences by leveraging AI to understand and predict individual preferences based on past interactions. Operational efficiency: By integrating diverse data sources and optimizing processes like supply chain management, RAG helps retailers streamline operations, reduce costs, and improve overall efficiency. For example, RAG aids in tracking shipments and optimizing logistics—a traditional pain point in the industry. Data utilization: It allows retailers to harness the power of big data by integrating and analyzing disparate data sources, providing actionable insights for informed decision-making. Customer engagement: RAG facilitates proactive customer engagement strategies through features like autonomous recommendation engines and hyper-personalized marketing campaigns, thereby increasing customer satisfaction and loyalty. In essence, RAG empowers retailers to harness AI's full potential to deliver superior customer experiences, optimize operations, and maintain a competitive edge in the dynamic retail landscape. But without a clear roadmap, even the most sophisticated AI solutions can falter. By pinpointing specific challenges—such as optimizing inventory management or enhancing customer service—retailers can leverage RAG to tailor solutions that deliver measurable business outcomes. Despite its transformative potential, retailers must first be AI-ready and able to integrate it in a way that enhances operational efficiency without overwhelming existing systems. To achieve this, retailers need to address data silos, ensure data privacy, and establish robust ethical guidelines for AI use. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, retailers will struggle to achieve the benefits they are looking for from AI. Embracing the future of retail with RAG and MongoDB By harnessing the power of data integration, precise use case definition, and cutting-edge AI technologies like RAG, retail enterprises can not only streamline operations but also elevate customer experiences to unprecedented levels of personalization and efficiency. Building a gen AI operational data layer (ODL) enables retailers to make the most of their AI-enabled applications. A data layer is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. As shown below in Figure 1, pulling data into a single database eliminates data silos, centralizes data management, and improves data integrity. Using MongoDB Atlas to unify structured and unstructured operational data offers a cohesive solution by centralizing all data management in a scalable, cloud-based platform. This unification simplifies data management, enhances data consistency, and improves the efficiency of AI and machine learning workflows by providing a single source of truth. With a flexible data schema, retailers can accommodate any data structure, format, or source—which is critical for the 80% of real-world data that is unstructured . Figure 1: Generative AI data layer As AI continues to evolve, the retail industry is poised to see rapid advancements, driven by the innovative use of technologies like RAG. The future of retail lies in seamlessly integrating data and AI to create smarter, more responsive business models. If you would like to learn more about RAG for Retail, visit the following resources: Presentation: Retrieval-Augmented Generation (RAG) to Supercharge Retail Enterprises White Paper: Enhancing Retail Operations with AI and Vector Search: The Business Case for Adoption The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.
Building Gen AI Applications Using Iguazio and MongoDB
AI can lead to major enterprise advancements and productivity gains. By offering new capabilities, they open up opportunities for enhancing customer engagement, content creation, process automation, and more. According to McKinsey & Company, generative Al has the potential to deliver an additional $200-340B in value for the banking industry . One popular use case is customer service, where gen AI chatbots have quickly transformed the way customers interact with organizations. They handle customer inquiries and provide personalized recommendations while empathizing with them and offering nuanced support tailored to individual needs. Another less obvious use case is fraud detection and prevention. AI offers a transformative approach by interpreting regulations, supporting data cleansing, and enhancing the efficacy of surveillance systems. These systems can analyze transactions in real-time and flag suspicious activities more accurately, which helps institutions prevent monetary losses. In this post, we introduce the joint MongoDB and Iguazio gen AI solution which allows for the development and deployment of resilient and scalable gen AI applications. Before diving into how it works and its value for you, let’s first discuss the challenges enterprises face when operationalizing gen AI applications. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Challenges to operationalizing gen AI Building an AI application starts with a proof of concept. However, enterprises need to successfully operationalize and deploy models in production to derive business value and ensure the solution is resilient. Doing so comes with its own set of challenges such as: Engineering challenges - Deploying gen AI applications requires substantial engineering efforts from enterprises. They need to maintain technological consistency throughout the operational pipeline, set up sufficient infrastructure resources, and ensure the availability of a team equipped with a comprehensive ML and data skillset. Currently, AI development and deployment processes are slow, time-consuming, and fraught with friction. LLM risks - When deploying LLMs, enterprises need to reduce privacy risks and comply with ethical AI standards. This includes preventing hallucinations, ensuring unbiased outputs, filtering out offensive content, protecting intellectual property, and aligning with regulatory standards. Glue logic and standalone solutions - The AI landscape is vibrant, and new solutions are frequently being developed. Autonomously integrating these solutions can create overhead for ops and data professionals, resulting in duplicate efforts, brittle architectures, time-consuming processes, and a lack of consistency. Iguazio and MongoDB together: High-performing and simplified gen AI operationalization The joint Iguazio and MongoDB solution leverages the innovation of these two leading platforms. The integrated solution allows customers to streamline data processing and storage, ensuring gen AI apps reach production while eliminating risks, improving performance, and enhancing governance. MongoDB for end-to-end AI data management MongoDB Atlas , an integrated suite of data services centered around a multi-cloud NoSQL database, enables developers to unify operational (structured and unstructured data), analytical, and AI data services into a single platform to streamline building AI-enriched applications . MongoDB’s flexible data model enables easy integration with different AI/ML platforms, allowing organizations to adapt to changes in the AI landscape without extensive infrastructure modifications. MongoDB meets the requirements of a modern AI and vector data store: Operational and unified: MongoDB’s ability to serve as the operational data store (ODS) enables financial institutions to efficiently handle large volumes of real-time operational data and unifies AI/vector data, ensuring AI/ML models use the most accurate information. It also enables organizations to meet compliance and regulatory requirements (e.g., 3DS2, ISO20022, PsD2) by the timely processing of large data volumes. Multi-modal: Alongside structured data, there's a growing need for semi-structured and unstructured data in gen AI applications. MongoDB's JSON-based multi-modal document model allows you to handle and process diverse data types, including documents, network/knowledge graphs, geospatial data, and time series data. Atlas Vector Search lets you search unstructured data. You can create vector embeddings with ML models and store and index them in Atlas for retrieval augmented generation (RAG), semantic search, recommendation engines, dynamic personalization, and other use cases. Flexible: MongoDB’s flexible schema design enables development teams to make application adjustments to meet changing data requirements and redeploy application changes in an agile manner. Vector store: Alongside the operational data store, MongoDB serves as a vector store with vector indexing and search capabilities for performing semantic analysis. To help improve gen AI experiences with greater accuracy and mitigate hallucination risks, using a RAG architecture together with the multi-modal operational data typically required by AI applications. Deployment flexibility: MongoDB can be deployed self-managed on-premise, in the cloud, or in a SaaS environment. Or deployed across a hybrid cloud environment for institutions not ready to be entirely on the public cloud. Iguazio’s AI platform Iguazio (acquired by McKinsey) is an AI platform designed to streamline the development of ML and gen AI applications in production at scale. Iguazio’s gen AI-ready architecture includes capabilities for data management, model development, application deployment, and LiveOps. The platform—now part of QuantumBlack Horizon , McKinsey’s suite of AI development tools—addresses enterprises’ two biggest challenges when advancing from gen AI proofs of concept to live implementations within business environments. Scalability: Ensures uninterrupted service regardless of workload demands, scaling gen AI applications when required. Governance: Gen AI guardrails mitigate risk by directing essential monitoring, data privacy, and compliance activities. By automating and orchestrating AI, Iguazio accelerates time-to-market, lowers operating costs, enables enterprise-grade governance, and enhances business profitability. Iguazio’s platform includes LLM customization capabilities, GPU provisioning to improve utilization and reduce cost, and hybrid deployment options (including multi-cloud or on premises). This positions Iguazio to uniquely answer enterprise needs, even in highly regulated environments, either in a self-serve or managed services model (through QuantumBlack, McKinsey’s AI arm). Iguazio’s AI platform provides: Structured and unstructured data pipelines for processing, versioning, and loading documents. Automated flow of data prep, tuning, validating, and LLM optimization to specific data efficiently using elastic resources (CPUs, GPUs, etc.). Rapid deployment of scalable real-time serving and application pipelines that use LLMs (locally hosted or external) as well as the required data integration and business logic. Built-in monitoring for the LLM data, training, model, and resources, with automated model re-tuning and RLHF. Ready-made gen AI application recipes and components. An open solution with support for various frameworks and LLMs and flexible deployment options (any cloud, on-prem). Built-in guardrails to eliminate risks and improve accuracy and control. Examples: Building with Iguazio and MongoDB #1 Building a smart customer care agent The joint solution can be used to create smart customer care agents. The diagram below illustrates a production-ready gen AI agent application with its four main elements: Data pipeline for processing the raw data (eliminating risks, improving quality, encoding, etc.). Application pipelines for processing incoming requests (enriched with data from MongoDB’s multi-modal store), running the agent logic, and applying various guardrails and monitoring tasks. Development and CI/CD pipelines for fine-tuning and validating models, testing the application to detect accuracy risk challenges, and automatically deploying the application. A monitoring system collecting application and data telemetry to identify resource usage, application performance, risks, etc. The monitoring data can be used to improve the application performance further through an RLHF (reinforcement learning from human feedback) integration. #2 Building a hyper-personalized banking agent In this example, accompanied by a demo video , we show a banking agent based on a modular RAG architecture that helps customers choose the right credit card for them. The agent has access to a MongoDB Atlas data platform with a list of credit cards and a large array of customer details. When a customer chats with the agent, it chooses the best credit card for them, based on the data and additional personal customer information, and can converse with them in an appropriate tone. The bank can further hyperpersonalize the chat to make it more appealing to the client and improve the odds of the conversion, or add guardrails to minimize AI hallucinations and improve interaction accuracy. Example customer #1: Olivia Olivia is a young client requesting a credit card. The agent looks at her credit card history and annual income and recommends a card with low fees. The tone of the conversation is casual. When Olivia asks for more information, the agent accesses the card data while retaining the same youthful and fun tone. Example customer #2: Miss Jessope The second example involves an older woman who the agent calls “Ms Jessope”. When asking for a new card, the agent accesses her credit card history to choose the best card based on her history. The conversation takes place in a respectful tone. When requesting more information, the response is more informative and detailed, and the language remains respectful. How does this work under the hood? As you can see from the figure below, the tool has access to customer profile data in MongoDB Atlas collection bfsi.user_data and is able to hyperpersonalize its response and recommendations based on various aspects of the customer profile. A RAG process is implemented using the Iguazio AI Platform with MongoDB Atlas data platform. The Atlas Vector Search capabilities were used to find the relevant operational data stored in MongoDB (card name, annual fees, client occupation, interest rates, and more) to augment the contextual data during the interaction itself to personalize the interaction. The virtual agent is also able to talk to another agent tool that has a view of the credit card data in bfsi.card_info (such as card name, annual and joining fees, card perks such as cashback, and more), to pick a credit card that would best suit the needs of the customer. To ensure the client gets the best choice of card, a guardrail is added that filters the cards chosen according to the data gathered by the agent as a built-in component of the agent tool. In addition, another set of guardrails is added to validate that the card offered suits the customer by comparing the card with the optimal ones recommended for the customer’s age range. This whole process is straightforward to set up and configure using the Iguazio AI Platform, with seamless integration to MongoDB. The user only needs to create the agent workflow and connect it to MongoDB Atlas, and everything works out of the box. Lastly, as you can see from the demo above, the agent was able to leverage the vector search capabilities of MongoDB Atlas to retrieve, summarize, and personalize the messaging on the card information and benefits in the same tone as the user’s. For more detailed information and resources on how MongoDB and Iguazio can transform your gen AI applications, we encourage you to apply for an exclusive innovation workshop with MongoDB's industry experts to explore bespoke modern app development and tailored solutions for your organization. Additionally, you can enjoy these resources: Start implementing gen AI applications in your enterprise today How Leading Industries are Transforming with AI and MongoDB Atlas The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.
The MongoDB AI Applications Program (MAAP) is Now Available
At MongoDB, everything starts with helping our customers solve their application and data challenges (regardless of use case). We talk to customers every day, and they’re excited about gen AI. But they’re also unsure how to move from concept to production, and need to control costs. So, finding the right way to adopt AI is critical. We’re therefore thrilled to announce the general availability of the MongoDB AI Applications Program (MAAP) ! A first-of-its-kind program, MAAP will help organizations take advantage of rapidly advancing AI technologies. It offers customers a wealth of resources to put AI applications into production: reference architectures and an end-to-end technology stack that includes integrations with leading technology providers, professional services, and a unified support system to help customers quickly build and deploy AI applications. Indeed, some early AI adopters found that legacy technologies can’t manage the multi-modal data structures required to power AI applications. This was compounded by a lack of in-house skills and the perceived risk of integrating disparate components without support. As a result, businesses couldn’t take advantage of AI advances quickly enough. Which is why we’re excited that MAAP is now available: the MAAP program and its ecosystem of companies addresses these challenges comprehensively. MAAP offers customers the right expertise and solutions for their use cases, and removes integration risk. Meanwhile, the MAAP ecosystem seamlessly integrates many of the world’s leading AI and tech organizations—a real value-add for customers. While the MAAP ecosystem is just getting started, it already includes tech leaders like Accenture, AWS, Google Cloud, and Microsoft Azure, as well as gen AI innovators Anthropic, Cohere, and LangChain. The result is a group of organizations that will enable customers to build differentiated, production-ready AI applications, while aiming to deliver substantial return on investment. Unlocking the power of data… It’s an understatement to say that the AI landscape is ever-changing. To keep pace with the latest developments and customer expectations, access to trusted collaborators and a robust support system are critical for organizations who want to innovate with AI. What’s more, innovating with AI can mean tackling data silos and overcoming limited in-house technical expertise—which MAAP solves for with a central architecture for gen AI applications, pre-configured integrations, and professional services to ensure organizations’ requirements are met. This framework provides flexibility for technical and non-technical teams alike, empowering them to leverage AI and company data for tasks specific to their department, no matter their preferred cloud or LLM. The MAAP ecosystem—representing industry leaders from every part of the AI stack—includes Accenture , Anthropic , Anyscale , Arcee AI , AWS , Cohere , Credal , Fireworks AI , Google Cloud , gravity9 , LangChain , LlamaIndex , Microsoft Azure , Nomic , PeerIslands , Pureinsights , and Together AI . MongoDB is uniquely qualified to bring together the solutions MAAP offers: MongoDB customers can use any LLM provider, we can run anywhere (on all major cloud providers, on premises, and at the edge), and MongoDB offers seamless integrations with a variety of frameworks and systems. Perhaps most importantly, thousands of customers already rely on MongoDB to power their mission-critical apps, and we have years of experience helping customers unlock the power of data. The ultimate aim of MAAP is to enable customers to get the most out of their data, and to ensure that they can confidently innovate with AI. A recent success is Anywhere Real Estate (NASDAQ: HOUS), the parent company of well-known brands like Century 21, Coldwell Banker, and Sotheby’s International Realty. Anywhere partnered with MongoDB to drive their digital transformation, and is now delving into the potential of MAAP to fast-track their AI adoption. By harnessing MongoDB’s expertise, Anywhere is set to future-proof its tech stack and to excel in an increasingly AI-driven landscape. “Generative AI is a game-changer for Anywhere, and we’re integrating it into our products with enthusiasm,” said Damian Ng, Senior Vice President of Technology at Anywhere. “MongoDB has been an invaluable partner, helping us rapidly explore and develop new approaches and opportunities. The journey ahead is exciting!” …and clearing the way for AI innovation MAAP offers customers a clear path to developing and deploying AI-enriched applications. The cornerstone of MAAP is MongoDB : applications are underpinned by MongoDB, which securely unifies real-time, operational, unstructured, and AI-related data without the need for bolt-on solutions. MongoDB’s open and integrated architecture provides easy access to the MAAP partner network and enables the extension and customization of applications. With MAAP, customers can: Accelerate their gen AI development with expert, hands-on support and services . MAAP expert services, combining the strengths of MongoDB Professional Services and industry-leading gen AI consultancies, will enable customers to rapidly innovate with AI. MAAP offers strategic guidance on roadmaps and skillsets, assists with data integration into advanced AI technologies, and can even develop production-ready applications. MAAP goes beyond development, empowering teams with best practices for securely integrating your data into scalable gen AI solutions, ensuring businesses are equipped to tackle future AI initiatives. Build high-performing gen AI applications that tackle industry-specific needs . Pre-designed architectures give customers repeatable, accelerated frameworks for building AI applications. Architectures are fully customizable and extendable to accommodate ever-evolving generative AI use cases, like retrieval-augmented generation (RAG) or advanced AI capabilities like Agentic AI and advanced RAG technique integrations. With MongoDB’s open and integrated platform at its core, innovation with MAAP’s composable architectures is unlimited, making it easy for customers to bring the power of leading AI platforms directly to their applications. Upskill teams to quickly—and repeatedly—build modern AI applications . MAAP customers have access to a variety of learning materials , including a dedicated MAAP GitHub library featuring integration code, demos, and a gen AI application prototype. These comprehensive resources will enable developers to build intelligent, personalized applications faster, while giving organizations the tools to expand their in-house AI expertise. With MAAP, customers have access to integration and development best practices that they can use for future gen AI projects. It’s early days, but there are wide-ranging indications that AI will impact everything from developer productivity to economic output. We’ve already seen customers use gen AI to speed modernization efforts, boost worker productivity with agents, unlock sales productivity , and power identity governance with natural language . In other words, AI is here to stay, and now is the time to take advantage of it. MAAP is designed to set customers up for AI success today and tomorrow: the program will be continuously enhanced with the latest technological advancements and industry best practices, to ensure that customers stay ahead of this rapidly evolving space. So please visit the MAAP page to learn more or to connect with the team! Our MAAP experts are happy to guide you on your AI journey and to show how the MongoDB AI Applications Program can help your organization.
The Converged AI and Application Datastore for Insurance
In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Scenario planning and flexible data layers One of the primary concerns for IT leaders and decision-makers in the insurance industry is making smart technology investments. The goal is to consolidate existing technology portfolios, which often include a variety of systems like SQL Server, Oracle, and IBM IMS. Consolidation helps reduce inventory and prepare for the future. But what does future-proofing really look like? Scenario planning is an effective strategy for future-proofing. This involves imagining different plausible futures and investing in the common elements that remain beneficial across all scenarios. For insurance companies, a crucial common thread is the data layer. By making data easier to work with, companies can ensure that their technology investments remain valuable regardless of how future scenarios unfold. MongoDB’s flexible developer data platform offers a distinct architectural advantage by making data easier to work with, regardless of the cloud vendor or AI application in use. This flexibility is vital for preparing for disruptive future scenarios, whether they involve regulatory changes, market shifts, or technological advancements. Watch now: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance The role of AI and data in insurance Generative AI is revolutionizing the insurance sector, offering new ways to manage and utilize data. According to Celent's 2023 Technology Insight and Strategy Survey, 33% of companies across different industries have AI projects in planning, 29% in development, and 19% in production (shown in Figure 1 below). This indicates a significant shift towards AI-driven solutions by insurers actively experimenting with gen AI. Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When 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 due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. Additionally, MongoDB’s flexibility enables insurers to integrate seamlessly with various technologies, making it a versatile and powerful solution for unstructured data management. For example, consider an insurance adjuster assessing damage from claim photos. Traditionally, this would require manually reviewing each image. With AI, the photos can be converted into vector embeddings and matched against a database of similar claims, drastically speeding up the process. This not only improves efficiency but also enhances the accuracy of assessments. The converged AI and application datastore with MongoDB Building a single view of data across various systems is a game-changer for the insurance industry. Data warehouses and data lakes have long provided single views of customer and claim data, but they often rely on historical data, which may be outdated. The next step is integrating real-time data with these views to make them more dynamic and actionable. A versatile database platform plays a crucial role in this integration. By consolidating data into a single, easily accessible view, insurance companies can ensure that various personas, from underwriters to data scientists, can interact with the data effectively. This integration allows for more responsive and informed decision-making, which is crucial for staying competitive in a rapidly evolving market. This can be achieved with a converged AI and application datastore, as shown in Figure 2 below. This is where operational data, analytics insights, and unstructured data become operationally ready for the applications that leverage AI. Figure 2: Converged AI and application datastore reference architecture The convergence of AI, data, and application datastores is reshaping the insurance industry. By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers 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. 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 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 learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence Head over to our quick-start guide to get started with Atlas Vector Search today. Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.
Anti-Money Laundering and Fraud Prevention With MongoDB Vector Search and OpenAI
Fraud and anti-money laundering (AML) are major concerns for both businesses and consumers, affecting sectors like financial services and e-commerce. Traditional methods of tackling these issues, including static, rule-based systems and predictive artificial intelligence (AI) methods, work but have limitations, such as lack of context and feature engineering overheads to keeping the models relevant, which can be time-consuming and costly. Vector search can significantly improve fraud detection and AML efforts by addressing these limitations, representing the next step in the evolution of machine learning for combating fraud. Any organization that is already benefiting from real-time analytics will find that this breakthrough in anomaly detection takes fraud and AML detection accuracy to the next level. In this post, we examine how real-time analytics powered by Atlas Vector Search enables organizations to uncover deeply hidden insights before fraud occurs. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The evolution of fraud and risk technology Over the past few decades, fraud and risk technology have evolved in stages, with each stage building upon the strengths of previous approaches while also addressing their weaknesses: Risk 1.0: In the early stages (the late 1990s to 2010), risk management relied heavily on manual processes and human judgment, with decision-making based on intuition, past experiences, and limited data analysis. Rule-based systems emerged during this time, using predefined rules to flag suspicious activities. These rules were often static and lacked adaptability to changing fraud patterns . Risk 2.0: With the evolution of machine learning and advanced analytics (from 2010 onwards), risk management entered a new era with 2.0. Predictive modeling techniques were employed to forecast future risks and detect fraudulent behavior. Systems were trained on historical data and became more integrated, allowing for real-time data processing and the automation of decision-making processes. However, these systems faced limitations such as, Feature engineering overhead: Risk 2.0 systems often require manual feature engineering. Lack of context: Risk 1.0 and Risk 2.0 may not incorporate a wide range of variables and contextual information. Risk 2.0 solutions are often used in combination with rule-based approaches because rules cannot be avoided. Companies have their business- and domain-specific heuristics and other rules that must be applied. Here is an example fraud detection solution based on Risk 1.0 and Risk 2.0 with a rules-based and traditional AI/ML approach. Risk 3.0: The latest stage (2023 and beyond) in fraud and risk technology evolution is driven by vector search. This advancement leverages real-time data feeds and continuous monitoring to detect emerging threats and adapt to changing risk landscapes, addressing the limitations of data imbalance, manual feature engineering, and the need for extensive human oversight while incorporating a wider range of variables and contextual information. Depending on the particular use case, organizations can combine or use these solutions to effectively manage and mitigate risks associated with Fraud and AML. Now, let us look into how MongoDB Atlas Vector Search (Risk 3.0) can help enhance existing fraud detection methods. How Atlas Vector Search can help A vector database is an organized collection of information that makes it easier to find similarities and relationships between different pieces of data. This definition uniquely positions MongoDB as particularly effective, rather than using a standalone or bolt-on vector database. The versatility of MongoDB’s developer data platform empowers users to store their operational data, metadata, and vector embeddings on MongoDB Atlas and seamlessly use Atlas Vector Search to index, retrieve, and build performant gen AI applications. Watch how you can revolutionize fraud detection with MongoDB Atlas Vector Search. The combination of real-time analytics and vector search offers a powerful synergy that enables organizations to discover insights that are otherwise elusive with traditional methods. MongoDB facilitates this through Atlas Vector Search integrated with OpenAI embedding, as illustrated in Figure 1 below. Figure 1: Atlas Vector Search in action for fraud detection and AML Business perspective: Fraud detection vs. AML Understanding the distinct business objectives and operational processes driving fraud detection and AML is crucial before diving into the use of vector embeddings. Fraud Detection is centered on identifying unauthorized activities aimed at immediate financial gain through deceptive practices. The detection models, therefore, look for specific patterns in transactional data that indicate such activities. For instance, they might focus on high-frequency, low-value transactions, which are common indicators of fraudulent behavior. AML , on the other hand, targets the complex process of disguising the origins of illicitly gained funds. The models here analyze broader and more intricate transaction networks and behaviors to identify potential laundering activities. For instance, AML could look at the relationships between transactions and entities over a longer period. Creation of Vector Embeddings for Fraud and AML Fraud and AML models require different approaches because they target distinct types of criminal activities. To accurately identify these activities, machine learning models use vector embeddings tailored to the features of each type of detection. In this solution highlighted in Figure 1, vector embeddings for fraud detection are created using a combination of text, transaction, and counterparty data. Conversely, the embeddings for AML are generated from data on transactions, relationships between counterparties, and their risk profiles. The selection of data sources, including the use of unstructured data and the creation of one or more vector embeddings, can be customized to meet specific needs. This particular solution utilizes OpenAI for generating vector embeddings, though other software options can also be employed. Historical vector embeddings are representations of past transaction data and customer profiles encoded into a vector format. The demo database is prepopulated with synthetically generated test data for both fraud and AML embeddings. In real-world scenarios, you can create embeddings by encoding historical transaction data and customer profiles as vectors. Regarding the fraud and AML detection workflow , as shown in Figure 1, incoming transaction fraud and AML aggregated text are used to generate embeddings using OpenAI. These embeddings are then analyzed using Atlas Vector Search based on the percentage of previous transactions with similar characteristics that were flagged for suspicious activity. In Figure 1, the term " Classified Transaction " indicates a transaction that has been processed and categorized by the detection system. This classification helps determine whether the transaction is considered normal, potentially fraudulent, or indicative of money laundering, thus guiding further actions. If flagged for fraud: The transaction request is declined. If not flagged: The transaction is completed successfully, and a confirmation message is shown. For rejected transactions, users can contact case management services with the transaction reference number for details. No action is needed for successful transactions. Combining Atlas Vector Search for fraud detection With the use of Atlas Vector Search with OpenAI embeddings, organizations can: Eliminate the need for batch and manual feature engineering required by predictive (Risk 2.0) methods. Dynamically incorporate new data sources to perform more accurate semantic searches, addressing emerging fraud trends. Adopt this method for mobile solutions, as traditional methods are often costly and performance-intensive. Why MongoDB for AML and fraud prevention Fraud and AML detection require a holistic platform approach as they involve diverse data sets that are constantly evolving. Customers choose MongoDB because it is a unified data platform (as shown in Figure 2 below) that eliminates the need for niche technologies, such as a dedicated vector database. What’s more, MongoDB’s document data model incorporates any kind of data—any structure (structured, semi-structured, and unstructured), any format, any source—no matter how often it changes, allowing you to create a holistic picture of customers to better predict transaction anomalies in real time. By incorporating Atlas Vector Search, institutions can: Build intelligent applications powered by semantic search and generative AI over any type of data. Store vector embeddings right next to your source data and metadata. Vectors inserted or updated in the database are automatically synchronized to the vector index. Optimize resource consumption, improve performance, and enhance availability with Search Nodes . Remove operational heavy lifting with the battle-tested, fully managed MongoDB Atlas developer data platform. Figure 2: Unified risk management and fraud detection data platform Given the broad and evolving nature of fraud detection and AML, these areas typically require multiple methods and a multimodal approach. Therefore, a unified risk data platform offers several advantages for organizations that are aiming to build effective solutions. Using MongoDB, you can develop solutions for Risk 1.0, Risk 2.0, and Risk 3.0, either separately or in combination, tailored to meet your specific business needs. The concepts are demonstrated with two examples: a card fraud solution accelerator for Risk 1.0 and Risk 2.0 and a new Vector Search solution for Risk 3.0, as discussed in this blog. It's important to note that the vector search-based Risk 3.0 solution can be implemented on top of Risk 1.0 and Risk 2.0 to enhance detection accuracy and reduce false positives. If you would like to discover more about how MongoDB can help you supercharge your fraud detection systems, take a look at the following resources: Revolutionizing Fraud Detection with Atlas Vector Search Card Fraud solution accelerator (Risk 1.0 and Risk 2.0) Risk 3.0 fraud detection solution GitHub repository Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.
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.
AI Apps: What the World Sees vs. What Developers See
Imagine you’re in the market for a new home in, say, Atlanta. And you’re on vacation in a different city. You see an amazing-looking house, whose design you love. You open up your favorite real estate app, snap a picture of this house, and type: “Find me a home that looks like this in Atlanta, in my price range, and within my budget, that’s also next to a park.” Seconds later, you’re served a list of homes that not only resemble this one, but match all your other specifications. This is what the world—specifically, consumers—expects when it comes to AI-powered applications. But when developers see the possibilities for these hyper-personalized, interactive, and conversational apps, they also see what goes into building them. A video showing the behind-the-scenes of an AI-powered real estate app. To make these advanced apps a reality, developers need to be able to unify operational and vector data . They also want to be able to use their preferred tools and popular LLMs. Most of all, developers are looking for a platform that makes their jobs easier—while, at the same time, providing a development experience that’s both seamless and secure. And it’s critical that developers have all of this. Because as in previous tech revolutions (the software revolution, the birth of the World Wide Web, the dawn of the smartphone, etc.), it’s developers who are leading the new AI revolution. And it’s developers who will use different kinds of data to push the boundaries of what’s possible. Take for instance audio data. Imagine a diagnostic application that records real-time sounds and turns those sounds into vectors. Then an AI model checks those sounds against a database of known issues: all of which pinpoints the specific sound that signals a potential problem that can now be fixed. Until recently, this kind of innovation wasn't possible. A video showing an AI-powered advanced diagnostics use case. This is also just the tip of the iceberg when it comes to the types of new applications that developers will build in this new era of AI. Especially when given a platform that not only makes working with operational and vector data easier, but provides an experience that developers actually love . To learn more about how developers are shaping the AI revolution, and how we at MongoDB not only celebrate them, but support them, visit www.mongodb.com/LoveYourDevelopers . There you can explore other AI use cases, see data requirements for building these more intelligent applications, discover developers who are innovating in this space, and get started with MongoDB Atlas for free .