Building Gen AI with MongoDB & AI Partners | December 2024

Gregory Maxson
January 16, 2025
#Partners #genAI

Now that 2024 is behind us, we can see clearly how much change, innovation, and progress there was across the AI landscape in 2024. For MongoDB, the year was particularly marked by collaboration with our AI partners, and by the possibilities that AI collaboration holds; as the saying goes, it takes a village. From the release of breakthrough tools and frameworks, to AI-enriched workflows (for both prototyping and production), together we empowered customers and developers alike to build cutting-edge AI applications.

To help you prepare for the rest of 2025, below is a selection of content developed by MongoDB’s Developer Relations team. This work will equip you with the knowledge (and tools!) from MongoDB and our AI partners to create the hottest AI applications in the new year.

Building an Agent with Fireworks.AI, MongoDB, and LangChain
Learn how to create an intelligent agent that combines Fireworks AI’s advanced capabilities, LangChain’s framework, and MongoDB's robust database. This guide walks you through developing an agent capable of reasoning and decision-making with structured and unstructured data.
Claude 3.5 and MongoDB: Revolutionizing Retrieval-Augmented Generation
Learn how Anthropic's Claude 3.5 integrates with MongoDB to enhance retrieval-augmented generation (RAG) pipelines. This post demonstrates using Claude for contextual and nuanced text generation while leveraging MongoDB Atlas for efficient data retrieval.
Build an AI Agent with LangGraph.js and MongoDB Atlas
Explore how LangGraph.js simplifies AI agent development for JavaScript and TypeScript developers. This tutorial showcases building an AI-powered agent and managing data with MongoDB Atlas for seamless functionality and scalability.
Ingesting Quantized Vectors with Cohere and MongoDB
Discover how to leverage Cohere’s quantized vector representations and MongoDB Atlas for efficient vector storage and retrieval. This guide demonstrates workflows for building scalable, high-performance applications that use vector embeddings for AI-driven solutions.

And if you’d like to dig into building with MongoDB and gen AI, explore our GenAI Showcase repository on GitHub for a wide range of sample projects, tools, and inspiration to kickstart your AI journey into 2025! Happy New Year—and happy building!

Welcoming new AI and tech partners

In December 2024, we welcomed six new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner!

Apigene

Apigene enables users to operate all software applications through a single AI assistant, providing complete control of popular services and platforms.

"We're excited to partner with MongoDB to bring natural language capabilities to Atlas users, transforming how teams interact with their data”, said Michal Geva, VP of Business of Apigene. “This collaboration makes database operations as intuitive as having conversations, empowering businesses to unlock Atlas’s full potential without complexity."

Bauplan

Bauplan is a programmable data lake where users can load, transform, query, run, schedule, and replay all from their code, driving superior cost-efficiency and less management from data teams.

“We're pretty darn excited about partnering up with MongoDB because the combination of Bauplan and MongoDB Atlas makes it so incredibly easy to build full-stack AI applications”, said Ciro Greco, CEO and founder of Bauplan. “One can build powerful applications like embedded analytics, feature stores, recommender systems, and RAG based search in a simple Python script. Zero infrastructure overhead, compute is purely serverless and everything's version controlled in the data lake by default.”

Botnoi

BOTNOI Group offers innovative AI technologies that enhance business operations such as a conversational AI chatbot for enterprise, speech-to-text, text-to-speech, and computer vision.

"We’re excited to announce our partnership with MongoDB”, said Piyoros Tungthamthiti, CTO of BOTNOI Group. “By integrating MongoDB Atlas, we’re enhancing Botnoi’s capabilities to deliver top-tier conversational AI performance. This collaboration will enable seamless data management, advanced analytics, and reliable system performance, ultimately providing greater value to our clients."

Jiva.ai

Jiva.ai is a zero-code platform for rapid multimodal AI development using structured and unstructured data.

"We are thrilled to join MongoDB's ecosystem and bring our no-code AI platform together with their powerful vector search and multimodal data capabilities,” said Dr. Manish Patel, CEO of Jiva.ai. “MongoDB enables us to help businesses rapidly transform complex data into intelligent solutions, democratizing AI development across industries. By combining Jiva.ai's patented model fusion technology with MongoDB's flexible document model, we're accelerating enterprise AI adoption and helping organizations unlock unprecedented insights from their data."

mple.ai

mple.ai is an AI-powered sales training platform for enterprises, designed to deliver scalable, measurable, and impactful training through role-plays and AI-driven evaluations.

"Our collaboration with MongoDB is redefining AI-driven team training”, said Riddhesh Ganatra, Co-Founder of mple.ai. “With MongoDB's reliable and scalable data solutions, we're delivering real-world scenario-based coaching to help organizations achieve faster, more impactful results."

TrueFoundry

TrueFoundryis a Kubernetes-based platform designed to simplify the process of building, deploying and scaling compound AI systems across any cloud or on-premise infrastructure.

“We’re thrilled to partner with MongoDB to accelerate the development of compound AI applications”, said Nikunj Bajaj, CEO of TrueFoundry. “With TrueFoundry’s powerful accelerators, including AI Gateway, Model Deployment & Finetuning, and RAG Framework, combined with MongoDB’s scalable vector database, enterprises can quickly build, deploy, and scale production-grade AI solutions. TrueFoundry’s platform ensures robust governance, cost optimization, and faster time to value, empowering enterprises to innovate efficiently and at scale.”

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.

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Leveraging an Operational Data Layer for Telco Success

The emergence of 5G network communication, IoT devices, edge computing, and AI have accelerated structural changes within the telecommunications industry, creating new needs and opportunities. To remain competitive, telcos must embrace this technology-driven transformation by defining a robust data strategy. Such a strategy should enhance operational efficiency and provide unique value to customers, and should ultimately enable telcos to set themselves apart from their competitors. All of this can be attained by leveraging an operational data layer (ODL) with MongoDB. Operating a consolidated ODL opens new business opportunities that telcos can incorporate into their value matrix, including customer support systems, AI-enriched applications, and IoT-oriented services. These unlocked capacities will help telecommunications companies succeed in a competitive market. Understanding the operational data layer An ODL is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. It acts as an intermediary between data producers and consumers. This architecture pattern is illustrated below: Figure 1. ODL sample reference architecture, using MongoDB In this diagram, MongoDB Atlas acts as the ODL, centrally integrating siloed data from multiple sources, including CRM, HR, and billing. Initially, data is extracted to the ODL, transformed according to established requirements, and then loaded to the MongoDB database. By means of delta loads, the ODL is kept in sync over time. Consuming applications, both operational and analytical, access the ODL through an API layer, which delivers a common set of methods for users, and enforces security standards throughout the organization. Enhancing operational efficiency with MongoDB and the ODL At its core, implementing an ODL with MongoDB provides access to a rich document model and a data developer platform that boosts operational efficiency and unlocks the value of previously siloed enterprise data. The ODL attains this efficiency through a set of key capabilities inherent to MongoDB. The ODL benefits from the flexibility of the document model that adapts its schema to any application requirement while supporting multiple data structures. This polymorphic structure allows variations from document to document liberating applications from rigid schemas and supporting merging from non-identical entities. Telcos gain speed in development—which translates to better performance—when accessing data through an ODL, as they avoid costly join operations required by legacy applications. MongoDB provides a unique place for data storage that can be accessed in a single database operation decreasing end-user response times. Telcos can leverage MongoDB’s versatility to cast multiple workloads, store any data type, and to adopt a rich query language that executes complex operations. Subsequently, the ODL accepts sophisticated query pipelines capable of processing text, images, videos, geospatial data, facet search, analytical transformations, time series, and more. Horizontal and vertical scalability empowers telcos to receive large data volumes and high traffic loads essential for modern applications. This mechanism is achieved through sharding, a process that partitions and distributes data across multiple nodes, accommodating fluctuating workload demands and enhancing overall system performance. An ODL running in MongoDB Atlas benefits from a multi-cloud strategy that allows deployments across multiple cloud providers. This approach mitigates vendor lock-in risks, grants global coverage, and adapts to infrastructure requirements—ensuring that applications adhere to cost constraints, achieve performance benchmarks, and maintain regulatory compliance. MongoDB provides a robust security framework for storing and managing sensitive data due to its built-in tools—including encryption, authentication, authorization, network security, and auditing—thus protecting data against information breaches. It also complies with important international regulations for telcos like the General Data Protection Regulation (GDPR) and the Payment Card Industry Data Security Standard (PCI DSS). MongoDB provides a modern data platform designed to build, manage and scale applications in a unified developer experience. The developer platform fosters innovation allowing developers to access a variety of features to manage their ODL including Atlas Vector Search, Atlas Monitoring, and Atlas Triggers, among others. Refer to our official documentation to learn more about MongoDB Atlas . Using the ODL to gain a competitive advantage Fostering operational efficiency through an ODL is the initial step toward opening a new business that will eventually translate into a competitive advantage. Accordingly, telcos need to develop their own strategies and capitalize on the benefits from these unlocked opportunities, differentiating themselves in the industry. Well-known telcos have already leveraged this approach, creating successful business outcomes. They consolidate single-view instances , concentrating information from different business lines—such as mobile, fixed lines, broadband, and TV/entertainment—into MongoDB Atlas. This environment is well-suited for building personalized customer management solutions, overcoming challenges with siloed data environments. These telcos choose MongoDB because it offers a flexible data model that facilitates data aggregation and horizontal scaling, allowing them to efficiently leverage customer data to build customer-centric applications. Additionally, leading telcos are leveraging AI to enhance their operations, safeguard their business, and improve their services. One prominent use of AI is fraud detection and prevention . This is a critical area that, if poorly managed, can lead to negative consequences like financial losses, unmeasurable reputational damage, and unhinged security network risks. A consolidated ODL serves as a gateway for implementing fraud detection measures. Nowadays, MongoDB’s platform is ingesting and storing terabytes of data from multiple platforms to leverage AI models, potentially saving millions of dollars for telcos. Refer to our ebook, Innovate with AI: The Future Enterprise , to learn more. Telcos are also capitalizing on their networks and the MongoDB ODL by effectively managing the vast amounts of data generated by IoT devices, and adding new end-to-end services. MongoDB is helping large telcos effectively implement IoT platforms supplying scalability for growing device demand, flexibility to manage data model changes, and automatic data tiering to reduce storage costs. These capabilities ultimately improve customer experiences and speed time to market for new applications. Furthermore, ODLs improve product catalog management systems, which are increasingly common in the industry due to telcos’ expanding their offering to a broader set of products, from phone plans to bundled entertainment services. ODLs upgrade the product catalog, allowing for real-time product personalization and analytics. MongoDB assists telcos in upgrading their product catalog systems, enabling advanced search capabilities, reducing development time, and supporting seasonal workload demand. Refer to our white paper, Implementing an Operational Data Layer for Product Catalog Modernization , to learn more. Finally, an ODL accelerates the modernization of monolithic relational database systems that struggle to manage exponential data growth and to adapt to evolving business needs. Telcos use MongoDB in their modernization efforts to deliver 3 to 5x faster operations, allowing scaling to millions of records per day, while at the same time reducing their costs—typically by 50% or more. Future directions This blog highlights how implementing an ODL with MongoDB can unlock telcos’ ability to achieve operational efficiency through the native capabilities of MongoDB and its cloud offering. This innovative architecture not only improves operations, but also unlocks business opportunities that are the foundation for new competitive advantages. These enhanced capabilities represent the backbone to consolidate telcos’ strategic positioning, ultimately differentiating from their competitors in powerful ways. Visit our MongoDB for Telecommunications solutions page to learn more. If you would like to learn more about implementing an ODL with MongoDB for your TELCO organization, visit the following resources: White paper: Implementing an Operational Data Layer White paper: Unleash Telco Transformation with an Operational Data Layer Head over to our quick-start guide to get started with Legacy Modernization today.

January 14, 2025

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Building a Unified Data Platform for Gen AI

In today’s digital-first world, data is the lifeblood of innovation and decision-making. Yet, businesses often find themselves constrained by outdated and fragmented systems that fail to meet the demands of a fast-paced, interconnected landscape. Legacy architectures—such as the 1970s-era mainframes still used in industries like banking—create inefficiencies, siloed data, and operational bottlenecks, leaving organizations struggling to deliver timely, actionable insights. The pressure to adapt is mounting, as customer expectations for real-time interactions and personalized services continue to grow. To thrive in this competitive environment, organizations must embrace a transformative approach to managing their data estates—one that integrates advanced technologies seamlessly. Unified data platforms powered by operational data layers (ODLs), generative AI (gen AI), and vector search are the solution. These innovations do more than just modernize data handling; they unlock new opportunities for agility, efficiency, and value creation, empowering businesses to make informed decisions, improve customer experiences, and drive growth. Let’s explore how these technologies are reshaping the way businesses consume, integrate, and leverage their data. Figure 1. Conceptual model of a Converged AI Data Store, showing multimodal data ingest. From stale to real-time data: The case for operational data layers In the rapidly evolving digital landscape, businesses can no longer afford to rely on outdated, batch-processed data. The demands of modern operations require instant access to fresh, accurate information. Yet many organizations continue to struggle with fragmented systems that deliver stale data, creating roadblocks in decision-making and customer engagement. This is where the concept of an ODL becomes transformative. Acting as a centralized hub, an ODL integrates data from multiple transactional systems in real-time, ensuring businesses have a unified and up-to-date view of their operations. Let’s explore how ODLs can revolutionize business processes: 1. Enabling real-time customer interactions Imagine a customer service representative handling a support call. Without real-time access to the latest data—such as a customer’s recent transactions, support history, or preferences—the interaction may feel disconnected and inefficient. An ODL solves this problem by consolidating and providing real-time data. For example, a telecom provider could use an ODL to ensure its agents have immediate access to recent billing information, technical issues reported, and ongoing resolutions. This not only empowers the agents but also leaves the customer with a seamless and satisfactory experience. 2. Streamlining account management Real-time data isn’t just about resolving customer issues; it’s also critical for proactive engagement. In industries like banking and retail, customers often need immediate updates on their accounts, such as current balances, transaction details, or loyalty points. By integrating APIs with the ODL, businesses can offer instantaneous responses to these queries. For instance, a retail bank could enable customers to check recent purchases or transfers through a chatbot that queries the ODL in real-time, delivering fast, accurate results. 3. Enhancing compliance and reporting Highly regulated industries, such as finance and healthcare, face additional challenges in managing large volumes of historical data for audits and compliance. Traditional systems often struggle to handle such demands, resulting in time-consuming manual processes. ODLs, when combined with gen AI, enable businesses to extract, summarize, and structure this data efficiently. For instance, a financial institution could use an ODL to generate compliance reports that pull data from diverse sources—both structured and unstructured—and ensure they meet regulatory standards with minimal manual intervention. 4. Supporting metadata and governance Another often overlooked advantage of an ODL is its ability to support metadata management and data governance. For large enterprises operating across multiple geographies, changes in localized data models are frequent and complex. An ODL can act as a centralized repository, capturing these updates and enabling advanced search functionalities for impact analysis. For example, a global enterprise could use an ODL to track changes in data definitions, understand usage patterns, and ensure compliance with governance policies across regions—all while reducing the risk of errors. The transformative power of gen AI and vector search As businesses transition to real-time data strategies powered by ODLs, the potential to unlock even greater insights lies in adopting cutting-edge tools like gen AI and vector search. These technologies are revolutionizing the way organizations consume and interpret data, enabling unprecedented efficiency and intelligence. Gen AI: By generating actionable insights, predictions, and content, gen AI enables businesses to turn static data into a strategic resource. For example, a retailer could use gen AI to analyze customer purchase histories and recommend personalized product bundles. Vector search: This technology translates high-dimensional data like text, images, and audio into vectors, enabling accurate, intuitive searches. For instance, healthcare providers can search for similar patient cases by symptoms, enhancing diagnostics and treatment planning. By incorporating these tools into an ODL, businesses can go beyond basic data integration, creating smarter, more agile operations capable of delivering value in real-time. Figure 2. Retrieval Augmented Generation (RAG) implementation, using the converged AI data store to provide context to the LLM prompt. New opportunities: Revolutionizing operations with gen AI and operational data layers The integration of gen AI and vector search into ODLs opens up a world of opportunities for businesses to enhance customer experience, streamline operations, and innovate at scale. Here’s how these technologies drive transformation: Enhanced data discovery: With vector search, organizations can quickly and accurately retrieve relevant data from massive datasets, simplifying complex searches. Improved customer experience: Gen AI–powered ODLs analyze customer behavior to deliver personalized recommendations, building stronger customer relationships. Increased operational efficiency: Automating routine data tasks with gen AI reduces manual effort, enabling teams to focus on strategic initiatives. Enhanced agility and innovation: By enabling rapid development of AI-driven applications, businesses can quickly adapt to market changes and stay ahead of the competition. As organizations embrace these capabilities, they position themselves to thrive in an increasingly competitive and data-driven world. Architectural options for data processing Modernizing data platforms requires a robust architecture that can handle both batch and real-time processing. Depending on their needs, organizations often choose between lambda or kappa architectures, and MongoDB can serve as a flexible operational layer for both. The lambda architecture The lambda architecture is ideal for organizations that need to process both batch and real-time data. It consists of three layers: Batch layer: This layer processes large volumes of historical data offline. Gen AI can enrich this data by generating insights and predictions. Speed layer: This layer handles real-time data streams, enabling immediate responses to changes. Serving layer: This layer combines batch and real-time data into a unified view, powered by MongoDB for seamless queries and data access. The kappa architecture For businesses focused on real-time analytics, the kappa architecture simplifies operations by using a single stream for data processing. MongoDB excels as the operational speed layer in this setup, supporting high-speed, real-time data updates enhanced by gen AI. By choosing the right architecture and leveraging MongoDB’s capabilities, businesses can ensure their data platforms are future ready. A journey toward data modernization Data modernization is a progressive journey, transforming businesses step by step into smarter, more agile systems. It begins with a basic operational data store , where read-heavy workloads are offloaded from legacy systems into MongoDB, boosting performance and accessibility. Next comes the enriched ODL , adding real-time analytics to turn raw data into actionable insights. Then, as needs grow, parallel writes enable MongoDB to handle write-heavy operations, enhancing speed and reliability. In the transition to the system of transaction , monolithic systems are replaced with agile microservices directly connected to MongoDB, simplifying operations and accelerating innovation. Finally, businesses reach the system of record , a domain-driven architecture where MongoDB provides unmatched scalability, flexibility, and efficiency. Each phase of this journey unlocks new opportunities, transforming data into a dynamic asset that powers innovation, operational excellence, and growth. Figure 3. A conceptual model showcasing the joint implementation of the Kappa (Data in Motion) and Lambda (Data at Rest) frameworks on MongoDB Atlas, utilizing Stream Processing for real-time data and Online Archive/Federation features for historical data management. The unified and intelligent future of data As businesses embrace real-time data architectures and advanced AI capabilities, the potential for innovation is boundless. With solutions like MongoDB, organizations can seamlessly integrate and harness their data, driving operational excellence and delivering exceptional customer experiences. Now is the time to modernize, innovate, and unlock the full potential of your data. Discover how TCS and MongoDB are harmonizing technologies for the future. Start your data modernization journey today!

January 15, 2025