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Building AI With MongoDB: Optimizing the Product Lifecycle with Real-Time Customer Data
Over the course of our Building AI with MongoDB blog post series, we’ve seen many organizations using AI to shape product development and support. Examples we’ve profiled so far include: Ventecon’s co-pilot helping product managers generate and refine specifications for new products Cognigy’s conversational AI solutions empowering businesses to provide instant and personalized customer service in any language and for any channel Kovai’s AI assistant helping users quickly discover information from product documentation and knowledge bases In this roundup of the latest AI builders, I’ll focus on three more companies innovating across the product lifecycle. We’ll start with Zetla, which helps teams prioritize product roadmaps using live customer insights and sentiment. Then I'll move on to Crewmate, which connects products to engaged communities of users. We’ll wrap with Ada, which helps product companies like Meta and Verizon better support their customers through AI-driven automation. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Zelta.AI: Prioritizing product roadmaps with data-driven customer analytics Today's digital economy means customer feedback streams into the enterprise from a multitude of physical and digital touchpoints. For product managers, it can seem an impossible task to synthesize this feedback into themes and priorities that underpin a coherent development plan everyone in the business commits to. This is the problem Zelta.ai was founded to address. Zelta uses generative AI to communicate insights on top of customer pain points found in companies’ most valuable asset: qualitative sources of customer feedback such as call transcripts and tickets, pulling directly from platforms like Gong, Zoom, Fireflies, Zendesk, Jira, Intercom, among others. Zelta leverages LLMs to process unstructured data and returns actionable insights for product teams The company’s engineering team uses a combination of fine-tuned OpenAI GPT-4, Cohere, and Anthropic models to extract, classify, and encode source data into trends and sentiment around specific topics and features. MongoDB Atlas is used as the data storage layer for source metadata and model outputs. “The flexibility MongoDB provides us has been unbelievable,” says Mick Cunningham, CTO and Co-Founder at Zelta AI. “My development team can constantly experiment with new features, just adding fields and evolving the data model as needed without any of the expensive schema migration pains imposed by relational databases.” Cunningham goes on to say, “We also make heavy use of the MongoDB aggregation pipeline for application-driven intelligence . Without having to ETL data out of MongoDB, we can analyze data in place to provide customers with real-time dashboards and reporting of trends in product feedback. This helps them make product decisions faster, making our service more valuable to them.” Looking forward, Zelta plans on creating its own custom models, and MongoDB will prove invaluable as a source of labeled data for supervised model training. Zelta is a member of the MongoDB AI Innovators program , taking advantage of free Atlas credits, access to technical support, and exposure to the wider MongoDB community. Crewmate: Helping brands connect with their communities In the digital economy, brands can spend millions of dollars growing online communities populated with highly engaged users of their products and services. However many of the tools used for building communities are third-party solutions that abstract away a brand’s visibility into user engagement. This is an issue Crewmate is working to address. Crewmate is a no-code builder for embedded AI-powered communities. The company’s builder provides customizable communities for brands to deploy directly onto their websites. Crewmate is already used today across companies in consumer packaged goods (CPG), B2B SaaS, gaming, Web3, and more. Crewmate starts by scraping a brand's website, along with open job postings and customer data from CRM systems. Scraped data is stored in its MongoDB Atlas database running on Google Cloud. An Atlas Trigger then calls OpenAI’s ada-002 embedding model, storing and indexing the vectorized encodings into Atlas Vector Search . An event-driven pipeline keeps the embeddings fresh by firing the Atlas Trigger as soon as new website data is inserted into the MongoDB database. Using context-aware semantic search powered by Atlas Vector Search, users hitting and browsing the community pages on a brand’s website are automatically served relevant content. This includes posts from social media feeds, forum discussions, job postings, special offers, and more. “I’ve used MongoDB in past projects and knew that its flexible document schema would allow me to store data of any structure. This is particularly important when ingesting many different types of data from my clients’ websites,” says Raj Thaker, CTO and Co-Founder of Crewmate. “The introduction of Atlas Vector Search and the Building Generative AI Applications tutorial gave me a fast, ready-made blueprint that brings together a database for source data, vector search for AI-powered semantic search, and reactive, real-time data pipelines to keep everything updated, all in a single platform with a single copy of the data and a unified developer API. This keeps my engineering team productive and my tech stack streamlined. Atlas also provides integrations with the fast-evolving AI ecosystem. So while today I’m using OpenAI models, I have the flexibility to easily integrate with other models, such as Llama, in the future.” Thaker goes on to say, “One of Crewmate’s major value creations is the insights brands can extract. Using the powerful and expressive MongoDB Query API I can process, aggregate, and analyze user engagement data so that brands can track community outreach efforts and conversions. They can generate this intelligence directly from their app data stored in MongoDB, avoiding the need to ETL it out into a separate data warehouse or data lake." Like Zelta, Crewmate is also part of MongoDB’s AI Innovators program . Ada: Revolutionizing customer service with AI-powered automations built on MongoDB Atlas Founded in 2016, Ada has become a leader in automating complex service interactions across any channel and modality. The company has raised close to $200 million, has 300 employees, and counts Meta, Verizon, and AT&T among its 300 customers. Mike Gozzo, Ada’s Chief Product and Technology Officer was interviewed at a recent MongoDB developer conference where he discussed the evolution of AI for customer service and the role MongoDB plays in Ada’s AI stack. Gozzo makes the point that while bots for customer service aren’t new, the huge advancements in transformer models and LLMs coupled with reinforcement learning from human feedback (RLHF) have made these assistants far more capable. Rather than just search for information, they can use advanced reasoning to solve customer problems. Asked why Ada selected MongoDB Atlas to underpin all its products, Gozzo says, “Having the flexibility and ability to just pivot on a dime was really important. We saw that as we advanced the company and brought in new channels and new modalities, having one data store that can be easily extended without crazy migrations and that would really support our needs was absolutely clear from MongoDB. We’ve always stayed the path with Atlas because the performance is there, the support from the team is great, and we believe in having less dependency on one central cloud vendor that MongoDB allows.” Gozzo goes on to say, “Using MongoDB means we’re not limited in how we source data if we want to build something new. We can query unstructured data and use it to train other models. We use generative AI effortlessly throughout our product stack to automate queries and provide support that goes beyond just answering multi-step queries. With MongoDB, we’re able to ship new products in just a few months.” Going forward, Ada is starting to use MongoDB Change Streams to build a distributed event processing system that powers bots and analytics. It is also exploring Queryable Encryption , which helps advance AI training while keeping conversations private. Getting started Check out our library of AI case studies to see the range of applications developers are building with MongoDB. Our 3-minute explainer video on Atlas Vector Search is a great way to assess what’s possible as you start on your journey to AI-powered apps.
Samsung Knox and MongoDB Join Forces to Secure the Future of Mobile Devices
In today's fast-paced and ever-evolving digital landscape, the need for robust and comprehensive security solutions has never been more important. Launched in 2013, Samsung Knox—the mobile device security platform from Samsung Electronics Mobile Experience (MX)— has set the standard for protecting business and enterprise devices, providing an unparalleled level of control from the application level down to the hardware. A presentation by Eunjoo Kim from Samsung Electronics MX at MongoDB.local Seoul 2023, outlined how the team is using MongoDB for the Samsung Knox Cloud solutions. The backbone of innovation The security platform’s mission is to ensure that business and enterprise customers can operate seamlessly without the threat of data breaches and cyberattacks. Since introduced in 2013, Knox has secured over 1 billion Samsung devices and is used to manage over 70 million devices. Trusted around the world, Knox has helped over 30,000 businesses achieve their goals at a global scale. Eunjoo Kim, Samsung Electronics' MX division As technology evolves, the platform team is committed to building an agile and rapid development environment with a microservices-oriented architecture to introduce new services. Starting in 2019, it has been using MongoDB to power its cutting-edge cloud-based platform. With MongoDB's powerful database capabilities, the platform has facilitated fast and efficient development across its dozens of MongoDB clusters worldwide. The synergy between the two companies enables Samsung’s development and operations teams to seamlessly build new microservices on top of MongoDB to solve new security challenges. Boosting developers' efficiency The infrastructure for Samsung Knox spans European and US regions, requiring a robust cluster management system. Using a replica set structure built on AWS EC2 instances and powered by MongoDB Enterprise Advanced, the team found a seamless architecture to provide better performance for local users and more resilience across regions. MongoDB Enterprise Advanced provides the Samsung Knox team with a comprehensive set of tools and features, including monitoring, real-time analytics, profiles, alerts, backups, maintenance, index manager, account manager, and more. "MongoDB Enterprise Advanced plays a critical role in securing the enterprise database infrastructure, ensuring smooth operations, and resolving issues quickly. What's more, all of these features are easily accessible and manageable through a web interface," says Eunjoo Kim. "MongoDB Ops Manager and the replica set structure result in a robust and easy-to-manage disaster recovery (DR) strategy, which is essential for enterprise services." Eunjoo Kim, Samsung Electronics' MX division All-around support for seamless migration Significant growth for Samsung Knox introduced new challenges. For example, the need to accommodate additional data such as IMEI numbers to support dual SIM cards in Samsung devices was a critical hurdle for developers. It required a major update to their existing device collection document, a task that could potentially add complexity to operations and development. To seamlessly deploy these new features into their existing security services, the team needed schema versioning patterns to quickly create new fields and update applications. MongoDB allowed the team to easily adapt their application logic to accommodate new data fields for the latest services. This approach also allowed them to control the pace of application migration and stabilize the process without disrupting their day-to-day operations and development efforts. Samsung Knox didn't go on this journey alone. MongoDB University , a free online education platform with hands-on courses, has been a key asset. It provides a wealth of knowledge on data modeling, as well as many other practical methods, including schema versioning techniques, that developers can use when working with MongoDB. "This resource simplifies the process of learning and using MongoDB, making it an invaluable tool for both new and experienced developers," emphasizes Eunjoo Kim. "With MongoDB University, we have been able to equip our team with the insights and skills required to realize the full potential of MongoDB and ensure successful adaptation to their evolving needs." Collaborative future with MongoDB Samsung Knox plans to continue working with MongoDB, not only in its technical infrastructure but also within its internal developer community. "Our team is focusing on spreading MongoDB knowledge among our developers. This means a deeper, company-wide understanding and use of MongoDB," says Eunjoo Kim. As the business expands and workloads become more complex, Samsung Knox is committed to working closely with MongoDB to stay at the forefront of providing security products and solutions that protect every device user.
How Canara HSBC Life Insurance Optimized Costs and Claims Processing with MongoDB
Since 2008, Canara HSBC Life Insurance has focused relentlessly on bringing a fresh perspective to an industry known more for stability and conservatism rather than innovation. Since its inception in 2008 as a joint venture between Canara Bank and HSBC Insurance, Canara HSBC Life Insurance has strived to differentiate itself from the competition through enhanced customer interactions, launching cutting-edge digital products, and integrating digital services that cater to the evolving needs of customers. For the past six years Chief Operating Officer, Mr. Sachin Dutta, has been on a mission to bring this customer-first mindset to the digital products and touchpoints his team creates. Speaking at MongoDB’s annual .local developer conference in Delhi, Dutta outlined Canara HSBC Life Insurance’s ongoing digital transformation journey, and how his team's focus on customer success and business efficiency led them to work with MongoDB for improved efficiencies and results. “I truly value the partnership we have with MongoDB. We are building a future-ready organization, and this partnership clearly helps us achieve our aim of reaching the last mile possible in customer servicing. Mr. Sachin Dutta, Chief Operating Officer, Canara HSBC Modernizing the architecture and driving developer efficiency Canara HSBC’s digital transformation was centered on three technical pillars: the cloud, analytics, and mobility. The company focused on creating a more integrated organization and automating manual processes within the system. “We try to remove human intervention with a life insurance policy delivered in seconds and claims that are settled virtually in seconds,” Dutta says. To get there, Canara HSBC Life Insurance had to move on from its existing architecture, which required multifaceted changes and several new implementations: Monolithic applications made alterations a time-consuming process A reliance on rigid relational databases prolonged development timelines, forcing developers to spend time wrangling data when they could be building better products for customers. The fully on-premises system had supported the organization in the past but required future-proofing to support growth and deliver a better customer experience. Because of this valuable development time and money were spent managing, patching, and scaling databases, rather than getting new products into the hands of customers. These technical issues impacted the speed of business, particularly during month-end and year-end data processing, when the volumes were high. In addition, batch processing stood in the way of creating the real-time availability of information customers wanted. Dutta and his senior team also realized that their existing infrastructure would make it more challenging to find the right talent in the market, as the existing infrastructure was increasingly becoming outdated. Dutta realized early on that, in order for Canara HSBC to attract and retain the best and brightest developers, the insurer had to offer the chance to work with the latest technologies. Platforms like MongoDB would be integral to this effort. “I want to create an organization that is attracting talent and where people start to enjoy their work, and that benefit then gets passed on to the customers, ” Mr. Dutta says. Looking to overhaul its existing infrastructure, Canara HSBC Life Insurance wanted to move fast and hire the talent required to best serve its end customers. Dutta summarized the situation succinctly: "We found that some of those relational structures that had worked for us would not take us through the next 10 years.” Migrating to a secure, fully managed database platform After evaluating the solutions on the market, the team decided to transition from their existing on-premises relational databases, like IBM DB2, MySQL, and Postgres, to MongoDB Atlas . In the last six years of my work, I’m pleased to say that MongoDB has seamlessly integrated all the processes in the backend. We migrated from a completely legacy-based setup to the new fully managed MongoDB service to enhance IT productivity Mr. Sachin Dutta The first stage of the journey was moving from monolithic applications and relational databases to a microservices architecture. With its flexible schema and capabilities for redundancy, automation, and scalability, MongoDB served as the best partner to help facilitate the transition. Next, the team moved to modernize key parts of the business, such as underwriting, freeing their data to power more automation in straight-through processing (STP) of policies and faster claims processing. The adoption of a hybrid cloud model shifted Canara HSBC Life Insurance away from on-premises databases to MongoDB Atlas. As a fully managed cloud database, MongoDB Atlas solves issues related to scalability, database management, and overall reliability. MongoDB Atlas is also cloud agnostic, giving the insurance company an option to work with Azure, AWS, and Google Cloud. Mongo Atlas’ BI Connector bridged the gap between MongoDB and traditional BI tools. This seamless integration allowed Canara HSBC Life Insurance to deploy its preferred reporting tools and, when coupled with MongoDB Atlas’ real-time analytics capability, made batch processing a thing of the past. Halving delivery times and driving business efficiencies Moving to MongoDB Atlas has had a profound impact on the breadth of digital experiences Canara HSBC Life Insurance can offer customers and the speed at which new products can be developed. Something that used to take months, with the implementation of our new tools could be completed in a couple of weeks or days Mr. Sachin Dutta And it’s not only the customer experience and product delivery that has benefited from the partnership. Canara HSBC Life Insurance has also realized substantial efficiency gains and savings as a result of working with MongoDB. We are leveraging artificial intelligence as a core capability to predict human behavior and auto-underwrite policies wherein around half of the policies issued today are issued by the system Mr. Sachin Dutta Highlighted results include: Straight-through processing (STP) surged from 37% to an impressive 60%. This is set to increase further with AI/ML integrations and rule suggestions. Policy issuance turnaround time improved by 60%. Efficiency in operations led to a 20% cost-saving per policy issuance. Canara HSBC experienced 2x top-line growth due to seamless integration with analytical tools. Looking ahead, Canara HSBC Life Insurance has already outlined three key areas where the MongoDB partnership will grow. First, Dutta wants to take advantage of MongoDB Atlas’ flexible document data model to collect and organize data on customers from across the business, making MongoDB Atlas the sole database at Canara HSBC Life Insurance and creating a true customer 360 data layer to power sophisticated data analytics. In financial services, this capability is referred to as know your customer (KYC). “We want to build a data layer that provides a unique experience to the customer after getting to know them,” he says. “That’ll help the company generate better NPS scores and retain customers.” Second, the adoption and integration of AI and machine learning tools also factor heavily into future plans. MongoDB Atlas, with its flexible schema, compatibility with various machine learning platforms, and AI-specific features — such as Vector Search and storage — is a good fit for the company. In Dutta's words, "We are going to scale up and capture the GenAI space.” Last, Dutta wants to take advantage of the MongoDB Atlas SQL interface, connectors, and drivers to augment business intelligence for reporting and precise SQL-based report conversions. Learn More about how MongoDB Works with global Insurers
MongoDB Doubles Down on Aotearoa as Part of Continued APAC Expansion
MongoDB is expanding its business in New Zealand to help Kiwi organisations build modern applications and take advantage of the AI opportunity that exists today. With hundreds of customers already in Aotearoa, including Pathfinder, Rapido, and Tourism Holdings, we're continuing to hire and invest to continue to grow our community in the country. Powering the next generation of modern applications Interest and excitement in AI, and particularly generative AI, has exploded. With a proud history of Innovation, it's not a surprise that many New Zealand companies are early adopters of this incredible technology. In fact, an AI Forum report has revealed that AI has the potential to increase New Zealand's GDP by as much as $54 billion by 2035. No matter what you think of the veracity of those bold predictions, one thing is sure: Almost every company is trying to figure out how to take advantage of data and software, to help them build better products, more efficiently and more quickly. Jake McInteer speaking at MongoDB.local Auckland As organisations transform into digital-first businesses, they’re faced with a growing list of application and data requirements. Modern applications are complex – they need to handle transactional workloads, app-driven analytics, full-text search, AI-enhanced experiences, stream data processing, and more. Companies are being asked to do this all while reducing data infrastructure sprawl, complexity and often also cut costs. What we are seeing globally is our developer data platform solves this challenge and complexity since it integrates all of the data services organisations need to build modern applications in a unified developer experience. Additionally, we also allow our customers to easily run anywhere in the world with over 110+ locations making us uniquely placed to enable Kiwi companies to adapt to a multicloud future. We also have strong local partnerships with all three cloud hyperscalers, all of which plan to open new cloud regions in New Zealand in the coming years. With the support of our cloud partners, in New Zealand we've already seen great adoption of MongoDB Atlas, including the largest established enterprises, through to cutting-edge startups. Here are a couple of examples. Pathfinder: Protecting vulnerable children Pathfinder , headquartered in Auckland, is a global leader in software development specialising in protecting vulnerable children. The company's mission centres on empowering law enforcement agencies with state-of-the-art technology, meticulously designed to combat the reprehensible crime of child exploitation. "We are committed to delivering investigators the most advanced tools. We cannot accept delays in removing a child from harm due to investigations being overwhelmed by large amounts of disparate data. In situations where every minute impacts a child's well-being, these tools must enable investigators to swiftly navigate data challenges, and rapidly apprehend perpetrators" said Bree Atkinson, CEO of Pathfinder Labs. Pathfinder’s Paradigm service is being built on MongoDB Atlas, running on AWS, and takes advantage of the wider developer data platform features in order to enable the next generation of data-driven investigative capabilities. By using MongoDB Atlas Vector Search , a native part of the MongoDB Atlas platform, the Pathfinder team are also able to match images and details within images (such as people and objects), classify documents and text, and build better search experiences for their users via semantic search. This enables Paradigm to efficiently aid law enforcement in identifying victims and apprehending offenders. Bree Atkinson, CEO of Pathfinder Labs, and Peter Pilly, DevOps Architect at Pathfinder Labs, with the MongoDB team in Auckland at the recent .local event "MongoDB Atlas allows our team to focus on our strengths: developing outstanding technology. It works with us not against us, enhancing integration which enables us to build better user experiences," said Peter Pilley, DevOps Architect at Pathfinder Labs. "Take MongoDB Atlas Vector Search, for example. Before MongoDB, we would have needed to incorporate multiple tools to achieve that functionality. Now we can handle it all from a single platform removing complexity and architecture that wasn't needed. With MongoDB Atlas, we're able to make data-driven decisions swiftly, boosting our productivity and decision-making speed." Peter's team at Pathfinder also uses MongoDB's performance advisor. They say it's like having an extra team member who suggests the best indexes for accessing their data, which is critical in an industry where getting to a specific piece of data could make all the difference. Rapido: Optimising B2B revenue and distribution Rapido has been utilising MongoDB Atlas for over five years. The team was originally part of MongoDB for Startups , a programme that offers startups free credits and technical advice to help them build faster and scale further. Their eagerness to adopt new technologies has enabled them to effectively harness MongoDB Atlas's evolving features. Working with the Accredo ERP system, Rapido has harnessed MongoDB Atlas to innovate in business-to-business (B2B) transactions. Using features like MongoDB Atlas Vector Search, the ' moreLikeThis ' operator, and MongoDB App Services, they've transformed business interactions, offering precise product recommendations and improved real-time visibility via change streams. Rapido's platform, which has processed orders collectively worth more than $100m to date, is essential for many wholesale businesses in New Zealand. Adam Holt, CEO of Rapido, summarises their experience: "Our journey with MongoDB Atlas has been transformative. By building on a cohesive developer data platform, we don't need to bolt-on and learn special technologies for every requirement. Continuously integrating new features keeps our platform advanced in the fast-paced B2B market. It's about leveraging technology to innovate and deliver better solutions to our clients." MongoDB expands in Aotearoa The increased demand from Kiwi organisations who are looking to innovate faster and take advantage of cutting-edge technologies, like AI, means MongoDB is now doubling down on its New Zealand footprint. Earlier this month, MongoDB established its local operations in Aotearoa, New Zealand. Jake McInteer , a native Kiwi, has officially transferred from MongoDB’s Australia business to lead the organisation in New Zealand. MongoDB already has a large, engaged community, more than 200 customers, and an extensive partner network. CEO of Lumin Max Ferguson presents at the Christchurch MongoDB user group We are incredibly excited about the opportunity to invest in and contribute to the Kiwi tech ecosystem, both to support local companies and help kiwi startups like Lumin and Marsello as well as established companies like Tourism Holdings , Figured , and Foster Moore . To support our growth, we have roles open on our Sales and Solutions Architecture team. If you are based in NZ and interested in joining our incredible team, working in our hybrid environment, please check out and apply for the roles here: Enterprise Account Executive, Acquisition Senior Solutions Architect Additionally, read here about the massive opportunity at MongoDB in APAC from our SVP Simon Eid.
How Atlas Edge Server Bridges the Gap Between Connected Retail Stores and the Cloud
Efficient operations and personalized customer experiences are essential for the success of retail businesses. In today's competitive retail industry, retailers need to streamline their operations, optimize inventory management, and personalize the customer experience to stay ahead. In a recent announcement at MongoDB .local London, we unveiled the private preview of MongoDB Atlas Edge Server , offering a powerful platform that empowers retailers to achieve their goals, even when low or intermittent connectivity issues may arise. What is edge computing, and why is it so relevant for retail? The retail industry's growing investment in edge computing, projected to reach $208 billion by 2023, confirms the strategic shift retailers are willing to take to reach new markets and enhance their offers. And for good reason — in scenarios where connectivity is unreliable, edge computing allows operations to continue uninterrupted. Edge computing is a strategic technology approach that brings computational power closer to where data is generated and processed, such as in physical retail stores or warehouses. Instead of relying solely on centralized data centers, edge computing deploys distributed computing resources at the edge of the network. The evolution of investments in edge computing reflects a journey from initial hesitation to accelerated growth. As edge computing continues to mature and demonstrate its value, retailers are likely to further embrace and expand their focus in bringing applications where the computing and data is as close as possible to the location where it's being used. Let’s dig into how MongoDB addresses the current challenges any retailer would experience when deploying or enhancing in-store servers using edge computing. Connected store: How MongoDB's versatile deployment from edge to cloud powers critical retail applications. Currently, many retail stores operate with an on-site server in place acting as the backbone for several critical applications within the store ecosystem. Having an on-site server means that the data doesn't have to travel over long distances to be processed, which can significantly reduce latency. This setup can often also be more reliable, as it doesn't depend on internet connectivity. If the internet goes down, the store can continue to operate since the essential services are running on the local network. This is crucial for applications that require real-time access to data, such as point-of-sale (POS) systems, inventory management, and workforce-enablement apps for customer service. The need for sync: Seamless edge-to-cloud integration The main driver for retailers taking a hybrid approach is that they want to experience the low latency and reliability of an on-site server coupled with the scalability and power of cloud computing for their overall IT stack. The on-site server ensures that the devices and systems that are critical to sales floor operations — RFID tags and readers for stock management, mobile scanners for associates, and POS systems for efficient checkout — remain functional even with intermittent network connectivity. This data must be synced to the retailer’s cloud-based application stack so that they have a view of what’s happening across the stores. Traditionally this was done with an end-of-day batch job or nightly upload. The aim for the next generation of these architectures is to give real-time access to the same data set, seamlessly reflecting changes made server-side or in the cloud. This needs to be achieved without a lag from the store being pushed to the cloud and without creating complex data sync or conflict resolution that needs to be built and maintained. These complexities may cause discrepancies between the online and offline capabilities of the store's operations. It makes sense that for any retailer wanting to benefit from both edge and cloud computing, it must simplify its architecture and focus on delivering value-added features to delight the customer and differentiate from their competitors. Low-latency edge computing with Atlas Edge Server and its different components to achieve data consistency and accuracy across layers This is when Atlas Edge Server steps in to bridge the gap. Edge Server runs on-premises and handles sync between local devices and bi-directional sync between the edge server and Atlas. It not only provides a rapid and reliable in-store connection but also introduces a tiered synchronization mechanism, ensuring that data is efficiently synced with the cloud. These devices are interconnected through synchronized data layers from on-premises systems to the cloud, simplifying the creation of mobile apps thanks to Atlas Device SDK , which supports multiple programming languages, development frameworks, and cloud providers. Additionally, Atlas Device Sync automatically handles conflicts, eliminating the need to write complex conflict-resolution code. In the below diagram, you can see how the current architecture for a connected store with devices using Atlas Device SDK and Atlas Device Sync would work. This is an ideal solution for devices to sync to the Atlas backend. A high-level overview of the Architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas when connectivity is unreliable. In a store with Atlas Edge Server, the devices sync to Atlas on-premises. All changes made on the edge or on the main application database are synced bidirectionally. If the store server goes offline or loses connectivity, the devices can still access the database and update it locally. The store can still run its operations normally. Then, when it comes back online, the changes on both sides (edge and cloud) are resolved, with conflict resolution built into the sync server. A high-level overview of the architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas solving connectivity issues by implementing an on-premises Atlas Edge Server. Deploying Atlas Edge Server in-store turns connected stores into dynamic, customer-centric hubs of innovation. This transformation produces advantageous business outcomes including: Enhanced inventory management — The hybrid model facilitates real-time monitoring of logistics, enabling retailers to meticulously track stock in store as shipments come in and sales or orders are processed. By processing data locally and syncing with the cloud, retailers gain immediate insights, allowing for more precise inventory control and timely restocking. Seamless operational workflows — The reliability of edge computing ensures essential sales tools — like RFID systems, handheld scanners, workforce apps, and POS terminals — remain operational even during connectivity hiccups. Meanwhile, the cloud component helps ensure that all data is backed up and accessible for analysis, leading to more streamlined store operations. Customized shopping experiences — With the ability to analyze data on-the-spot (at the edge) and harness historical data from the cloud, retailers can create highly personalized shopping experiences. This approach enables real-time, tailored product recommendations and promotions, enhancing customer engagement and satisfaction. Conclusion With Atlas Edge Server, MongoDB is committed to meeting the precise needs of modern retail stores and their diverse use cases. Lacking the seamless synchronization of data between edge devices and the cloud, delivering offline functionality that enables modern, next-generation workforce applications, as well as in-store technologies like POS systems, is daunting. Retailers need ready-made solutions so they don't have to deal with the complexities of in-house, custom development. This approach allows them to channel their development efforts towards value-added, differentiating features that directly benefit their customers by improving their in-store operations. With this approach, we aim to empower retailers to deliver exceptional customer experiences and thrive in the ever-evolving retail landscape. Ready to revolutionize your retail operations with cutting-edge technology? Discover how MongoDB's Atlas Edge Server can transform your store into a dynamic, customer-centric hub. Don't let connectivity issues hold you back. Embrace the future of retail with Atlas Edge Server!
India: A Cornerstone of Growth for MongoDB Technical Services
India has emerged as a cornerstone in our MongoDB Technical Services growth story, marked by the team’s 100% growth in just two years. Bengaluru has been at the forefront of our expansion, witnessing an incredible increase in personnel and the addition of new teams and functions to support our developer data platform. This highlights Bengaluru’s emerging role in providing critical technical assistance to our customers and partners. Gurugram has also played a crucial role in the growth of Technical Services in India. This growth underscores Gurugram’s increasing significance as a thriving hub for MongoDB Technical Services. MongoDB’s continuing investment in expanding Technical Services in India reflects the substantial impact the team has had on MongoDB's customer success. APAC Technical Services team members A look into Technical Services We have multiple customer-facing Technical Services teams in India, each with unique roles and responsibilities. From specific product support to support for MongoDB services and Atlas cluster deployments, each team seeks individuals with strong critical thinking skills who can quickly detect, resolve, or escalate complex issues that may span various aspects of MongoDB's products and services. Our Technical Services teams are committed to delivering exceptional support to our customers through each team’s unique focus. Building together across teams and departments As part of their role, Technical Services Engineers (TSEs) need to partner with other supporting functions within MongoDB to ensure seamless operations and exceptional customer support. TSEs proactively identify issues that may require escalation and involve Escalation Managers accordingly. They also identify opportunities for improvement within the MongoDB product ecosystem and pass on any feedback, feature requests, and customer insights to our Product Management and Customer Success teams. India Technical Services team members The Technical Services team is highly collaborative and works together to solve customer problems. While each sub-team within Technical Services focuses on specific areas of expertise, there are numerous intersections that require cross-team collaboration. This collaborative approach enables team members to learn and build new skills by exploring different areas of interest. Excellence centers We also take pride in the technical excellence centers that we have built between our teams in India. These centers are part of our global Technical Experts ecosystem. Technical Experts help us train the Technical Services team, liaise with the development teams, and highlight pain points to the Product Management team, amongst other responsibilities. Learning and development We believe that an engineer's journey goes beyond a career and is about gaining knowledge, skills, and experiences. MongoDB is an integral part of this journey, offering a platform for continuous growth and development. New hires undergo comprehensive onboarding and training to gain a holistic understanding of the MongoDB ecosystem. Engineers are encouraged to participate in cross-team rotations and have access to various learning platforms, including O’Reilly Learning and internal Product Readiness training. We collaborate closely with our Technical Services Knowledge and Training teams to deliver training sessions, fostering both technical and presentation skills and an observational learning culture. Engineers are also provided with “protected time” to focus on their individual learning plan, allowing them to focus on delivering projects, prepping for certifications, growing their expertise in a specific area, or working in collaborative cross-skill focus groups. Periodic hackathons offer a platform for innovation, encourage collaboration, and contribute to team-wide problem-solving efforts. In addition, MongoDB User Groups and .local events allow the team to showcase and share their knowledge externally. A TSE speaking at MongoDB.local Mumbai By creating a learning environment where engineers can grow and achieve their goals, both our team members and the business thrive. Enhancing support across time zones An exciting development in our Technical Services journey is the introduction of the Swing Shift in India. This strategic initiative leverages India's engineering talent to enhance support during India and EMEA hours, further augmenting support for the surrounding regions and improving service continuity for APAC and EMEA customers. We continue to hire in both our Bengaluru and Gurugram offices. If you’re someone who is technically talented, enjoys problem-solving, loves working with customers, and values your personal and professional growth, I encourage you to explore open roles on our careers site .
A Year of Thrill: Celebrating the New MongoDB University
Staying ahead in the ever-evolving tech world is like being on a rollercoaster - it’s exciting but it can also make your head spin! When we set out to revamp MongoDB University , we wanted to provide developers with frictionless access to the learning content they needed to conquer their challenges. It’s been one year since the launch and we are over the moon about how far the new MongoDB University has come - a one-stop hub with fresh certifications and new content, all available online. But none of this would have been possible without the incredible support of our engaged learners who have embarked on this ride with us. Our commitment to delivering top-notch educational resources has been nothing short of award-winning, earning us the prestigious Silver Excellence Award from the Brandon Hall Group in the category of ‘Best Advance in Creating an Extended Enterprise Learning Program’. The success of the new University has also been featured at industry events, including Cognition and the Customer Education Management Association Conference. So, let’s buckle up and take a tour through the revamped MongoDB University! New content With over 1,000 learning assets, including videos, hands-on labs, code recaps, quizzes, and courses, there’s something for everyone. Plus, now we have you covered with language subtitles in Chinese (Traditional and Simplified), Korean, Spanish, German, Japanese, Italian, French, and Portuguese. The best part? All of the content is free, online, and you can take your time and learn at your own pace. Let’s explore three of our newest courses: Data Modeling for MongoDB: This course guides you through the foundational steps of creating an effective data model in MongoDB, including identifying entities and workloads, mapping and modeling relationships between entities, and using key schema design patterns. Atlas Essentials: In this course, you’ll gain the foundational knowledge and skills needed to use MongoDB Atlas, the multi-cloud developer data platform. MongoDB for SQL Professionals: This course will help you leverage your SQL skills to get started with MongoDB quickly. You can practice what you learn and gain valuable real-world skills with labs hosted in our in-browser development environment. The new experience allows you to explore hands-on exercises as part of our courses, or you can dive directly into a standalone lab . The labs include step-by-step instructions that guide you through each scenario and even provide hints along the way. And for those looking for nuggets of MongoDB wisdom, explore the catalog of over 30 Learning Bytes . These short videos cover a wide variety of topics and are designed to help you get the knowledge you need quickly. New certifications Our freshly revamped certifications are recognized by professional institutions and are your ticket to having your knowledge and skills formally validated and recognized by MongoDB. They are a great way to elevate yourself in your current role and increase your marketability for future roles. Certifications come with bragging rights, inclusion in the Credly Talent Directory, and a shiny Credly badge that makes it easy for you to share your achievement. So, let’s explore the two new certifications: MongoDB Associate Developer: Certify that you possess the essential skills to create beginner-level applications utilizing MongoDB as a backing database for Java, Python, C#, PHP, or Java applications. MongoDB Associate Database Administrator: Validate your MongoDB database administration skills by certifying your knowledge of building, supporting, and securing MongoDB infrastructure. And if you need a boost, once you complete one of the certification learning paths you will automatically unlock a 50% discount on a certification exam. Educators and students can check out the Academia program to learn how to receive a free exam. All aboard! This is just the beginning of the adventure and we are excited for what is yet to come. So, fasten your seatbelt, and let’s keep learning together! With over 1,000 learning assets, MongoDB University has what you need to pick up new skills and advance your career. Explore free courses, practice with hands-on labs, and earn MongoDB certifications.
Building AI with MongoDB: Retrieval-Augmented Generation (RAG) Puts Power in Developers’ Hands
As recently as 12 months ago, any mention of retrieval-augmented generation (RAG) would have left most of us confused. However, with the explosion of generative AI, the RAG architectural pattern has now firmly established itself in the enterprise landscape. RAG presents developers with a potent combination. They can take the reasoning capabilities of pre-trained, general-purpose LLMs and feed them with real-time, company-specific data. As a result, developers can build AI-powered apps that generate outputs grounded in enterprise data and knowledge that is accurate, up-to-date, and relevant. They can do this without having to turn to specialized data science teams to either retrain or fine-tune models — a complex, time-consuming, and expensive process. Over this series of Building AI with MongoDB blog posts, we’ve featured developers using tools like MongoDB Atlas Vector Search for RAG in a whole range of applications. Take a look at our AI case studies page and you’ll find examples spanning conversational AI with chatbots and voice bots, co-pilots, threat intelligence and cybersecurity, contract management, question-answering, healthcare compliance and treatment assistants, content discovery and monetization, and more. Further reflecting its growing adoption, Retool’s State of AI survey from a couple of weeks ago shows Atlas Vector Search earning the highest net promoter score (NPS) among developers . Check out our AI resource page to learn more about building AI-powered apps with MongoDB. In this blog post, I’ll highlight three more interesting and novel use cases: Unlocking geological data for better decision-making and accelerating the path to net zero at Eni Video and audio personalization at Potion Unlocking insights from enterprise knowledge bases at Kovai Eni makes terabytes of subsurface unstructured data actionable with MongoDB Atlas Based in Italy, Eni is a leading integrated energy company with more than 30,000 employees across 69 countries. In 2020, the company launched a strategy to reach net zero emissions by 2050 and develop more environmentally and financially sustainable products. Sabato Severino, Senior AI Solution Architect for Geoscience at Eni, explains the role of his team: “We’re responsible for finding the best solutions in the market for our cloud infrastructure and adapting them to meet specific business needs.” Projects include using AI for drilling and exploration, leveraging cloud APIs to accelerate innovation, and building a smart platform to promote knowledge sharing across the company. Eni’s document management platform for geosciences offers an ecosystem of services and applications for creating and sharing content. It leverages embedded AI models to extract information from documents and stores unstructured data in MongoDB. The challenges for Severino’s team were to maintain the platform as it ingested a growing volume of data — hundreds of thousands of documents and terabytes of data — and to enable different user groups to extract relevant insights from comprehensive records quickly and easily. With MongoDB Atlas , Eni users can quickly find data spanning multiple years and geographies to identify trends and analyze models that support decision-making within their fields. The platform uses MongoDB Atlas Search to filter out irrelevant documents while also integrating AI and machine learning models, such as vector search, to make it even easier to identify patterns. “The generative AI we’ve introduced currently creates vector embeddings from documents, so when a user asks a question, it retrieves the most relevant document and uses LLMs to build the answer,” explains Severino. “We’re looking at migrating vector embeddings into MongoDB Atlas to create a fully integrated, functional system. We’ll then be able to use Atlas Vector Search to build AI-powered experiences without leaving the Atlas platform — a much better experience for developers.” Read the full case study to learn more about Eni and how it is making unstructured data actionable. Video personalization at scale with Potion and MongoDB Potion enables salespeople to personalize prospecting videos at scale. Already over 7,500 sales professionals at companies including SAP, AppsFlyer, CaptivateIQ, and Opensense are using SendPotion to increase response rates, book more meetings, and build customer trust. All a sales representative needs to do is record a video template, select which words need to be personalized, and let Potion’s audio and vision AI models do the rest. Kanad Bahalkar, co-founder and CEO at Potion explains: “The sales rep tells us what elements need to be personalized in the video — that is typically provided as a list of contacts with their name, company, desired call-to-action, and so on. Our vision and audio models then inspect each frame and reanimate the video and audio with personalized messages lip-synced into the stream. Reanimation is done in bulk in minutes. For example, one video template can be transformed into over 1,000 unique video messages, personalized to each contact.” Potion’s custom generative AI models are built with PyTorch and TensorFlow, and run on Amazon Sagemaker. Describing their models, Kanad says “Our vision model is trained on thousands of different faces, so we can synthesize the video without individualized AI training. The audio models are tuned on-demand for each voice.” And where does the data for the AI lifecycle live? “This is where we use MongoDB Atlas ,” says Kanad. “We use the MongoDB database to store metadata for all the videos, including the source content for personalization, such as the contact list and calls to action. For every new contact entry created in MongoDB, a video is generated for it using our AI models, and a link to that video is stored back in the database. MongoDB also powers all of our application analytics and intelligence . With the insights we generate from MongoDB, we can see how users interact with the service, capturing feedback loops, response rates, video watchtimes, and more. This data is used to continuously train and tune our models in Sagemaker." On selecting MongoDB Kanad says, “I had prior experience of MongoDB and knew how easy and fast it was to get started for both modeling and querying the data. Atlas provides the best-managed database experience out there, meaning we can safely offload running the database to MongoDB. This ease-of-use, speed, and efficiency are all critical as we build and scale the business." To further enrich the SendPotion service, Kanad is planning to use more of the developer features within MongoDB Atlas. This includes Atlas Vector Search to power AI-driven semantic search and RAG for users who are exploring recommendations across video libraries. The engineering team is also planning on using Atlas Triggers to enable event-driven processing of new video content. Potion is a member of the MongoDB AI Innovators program. Asked about the value of the program, Kanad responds, “Access to free credits helped support rapid build and experimentation on top of MongoDB, coupled with access to technical guidance and support." Bringing the power of Vector Search to enterprise knowledge bases Founded in 2011, Kovai is an enterprise software company that offers multiple products in both the enterprise and B2B SaaS arena. Since its founding, the company has grown to nearly 300 employees serving over 2,500 customers. One of Kovai’s key products is Document360, a knowledge base platform for SaaS companies looking for a self-service software documentation solution. Seeing the rise of GenAI, Kovai began developing its AI assistant, “Eddy.” The assistant provides answers to customers' questions utilizing LLMs augmented by retrieving information in a Document360 knowledge base. During the development phase Kovai’s engineering and data science teams explored multiple vector databases to power the RAG portion of the application. They found the need to sync data between its system-of-record MongoDB database and a separate vector database introduced inaccuracies in answers from the assistant. The release of MongoDB Atlas Vector Search provided a solution with three key advantages for the engineers: Architectural simplicity: MongoDB Vector Search's architectural simplicity helps Kovai optimize the technical architecture needed to implement Eddy. Operational efficiency: Atlas Vector Search allows Kovai to store both knowledge base articles and their embeddings together in MongoDB collections, eliminating “data syncing” issues that come with other vendors. Performance: Kovai gets faster query response from MongoDB Vector Search at scale to ensure a positive user experience. Atlas Vector Search is robust, cost-effective, and blazingly fast! Said Saravana Kumar, CEO, Kovai, when speaking about his team's experience Specifically, the team has seen the average time taken to return three, five, and 10 chunks between two and four milliseconds, and if the question is a closed loop, the average time reduces to less than two milliseconds. You can learn more about Kovai’s journey into the world of RAG in the full case study . Getting started As the case studies in our Building AI with MongoDB series demonstrate, retrieval-augmented generation is a key design pattern developers can use as they build AI-powered applications for the business. Take a look at our Embedding Generative AI whitepaper to explore RAG in more detail.
MongoDB Atlas AWS CloudFormation and CDK Integration Expansion
At MongoDB, we meet our developers where they’re at and offer multiple ways to get started and work with MongoDB Atlas . Since our GA launch of the MongoDB Atlas integration with the AWS CloudFormation Registry at the start of this year, users have had the freedom to manage their MongoDB Atlas resources using familiar YAML or JSON CloudFormation Templates. This provided developers and DevOps teams the core Infrastructure as Code (IaC) benefits: enhanced automation, version control, infrastructure consistency, and improved compliance. In addition to these updates, we went further and announced support for CDK at MongoDB.Local NYC in June 2023, which allowed development teams to leverage MongoDB Atlas resources natively in the language of their choice: JavaScript, TypeScript, Python, Java, Go, and C#. Today, just ahead of AWS re:Invent , we are excited to announce several key improvements and expansions to our AWS CloudFormation and CDK integrations that we hope will continue to make developers' lives even easier. New MongoDB Atlas resources on the AWS CloudFormation Registry Nine new MongoDB Atlas Resources have been published including Federated Database Instance , Serverless Private Endpoint , Programmatic API Keys Management , MongoDB Atlas Gov Support , and MongoDB Atlas Organization Management . This brings the total MongoDB Atlas Resources count on CloudFormation Registry to 42 and allows developers to do more with MongoDB Atlas and AWS CloudFormation. AWS region expansion Are you a developer based in or have your end customers in Hyderabad India , Melbourne Australia , Spain , Switzerland , or the UAE ? The good news, we have published all 42 Atlas Resources in each of these new AWS regions as well. Benefits include reduced latency and improved compliance with data sovereignty regulations. This brings the total MongoDB Atlas availability from 22 to 27 AWS regions on the AWS CloudFormation and CDK. New CDK level 3 resources The CDK provides different levels of abstraction for defining cloud resources: L1 constructs, which are direct mappings to AWS CloudFormation resources, and higher-level constructs like L2 and L3, which can provide high levels of abstraction. L3 constructs, also known as "Design Patterns" or "High-Level Constructs," combine multiple resources together in commonly used architectures with intelligent defaults, saving developers from manually having to glue L1 and L2 constructs together each time. Hence, we are happy to announce several new AWS CDK L3 resources including support for MongoDB Atlas Serverless . Migration to the Atlas Go SDK Lastly, we are delighted to have migrated our AWS CloudFormation resources to the new Atlas Go SDK . This is the middle layer that translates AWS CloudFormation calls to the Atlas Admin API (which is ultimately responsible for provisioning your MongoDB Atlas infrastructure). This migration goes a long way in accelerating our internal development velocity and enabling us to publish more MongoDB Atlas Resources on AWS CloudFormation soon after they go GA. Learn more about the key benefits of the Atlas Go SDK . Start building today These MongoDB Atlas integrations with AWS CloudFormation are free and open-source, licensed under the Apache License 2.0 . Users only pay for underlying MongoDB Atlas and AWS resources created and can get started building with the Atlas always-free tier ( M0 clusters ). Getting started today is faster than ever with MongoDB Atlas and AWS CloudFormation. We can’t wait to see what you will build next. Learn more on our MongoDB Atlas and AWS CloudFormation page.
Building AI with MongoDB: Improving Productivity with WINN.AI’s Virtual Sales Assistant
Better serving customers is a primary driver for the huge wave of AI innovations we see across enterprises. WINN.AI is a great example. Founded in November 2021 by sales tech entrepreneur Eldad Postan Koren and cybersecurity expert Bar Haleva, their innovations are enabling sales teams to improve productivity by increasing the time they focus on customers. WINN.AI orchestrates a multimodal suite of state-of-the-art models for speech recognition, entity extraction, and meeting summarization, relying on MongoDB Atlas as the underlying data layer. I had the opportunity to sit down with Orr Mendelson, Ph.D., Head of R&D at WINN.AI, to learn more. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Tell us a little bit about what WINN.AI is working to accomplish Today’s salespeople spend over 25% of their time on administrative busywork - costing organizations time, money, and opportunity. We are working to change that so that sales teams can spend more time solving their customer’s problems and less on administrative tasks. At the heart of WINN.AI is an AI-powered real-time sales assistant that joins your virtual meetings. It detects and interprets customer questions, and immediately surfaces relevant information for the salesperson. Think about retrieving relevant customer references or competitive information. It can provide prompts from a sales playbook, and also make sure meetings stay on track and on time. After concluding, WINN.AI extracts relevant information from the meeting and updates the CRM system. WINN.AI integrates with the leading tools used by sales teams, including Zoom, Hubspot, Salesforce, and more. Can you describe what role AI plays in your application? Our technology allows the system to understand not only what people are saying on a sales call, but also to specifically comprehend the context of a sales conversation, thus optimizing meeting summaries and follow-on actions. This includes identifying the most important talking points discussed in the meeting, knowing how to break down the captured data into different sales methodology fields (MEDDICC, BANT, etc.), and automatically pushing updates to the CRM. What specific AI/ML techniques, algorithms, or models are utilized in the application? We started out building and training our own custom Natural Language Processing (NLP) algorithms and later switched to GPT 3.5 and 4 for entity extraction and summarization. Our selection of models is based on specific requirements of the application feature – balancing things like latency with context length and data modality. We orchestrate all of the models with massive automation, reporting, and monitoring mechanisms. This is developed by our engineering teams and assures high-quality AI products across our services and users. We have a dedicated team of AI Engineers and Prompts Engineers that develop and monitor each prompt and response so we are continuously tuning and optimizing app capabilities. How do you use MongoDB in your application stack? MongoDB stores everything in the WINN.AI platform. Organizations and users, sessions, their history, and more. The primary driver for selecting MongoDB was its flexibility in being able to store, index, and query data of any shape or structure. The database fluidly adapts to our application schema, which gives us a more agile approach than traditional relational databases. My developers love the ecosystem that has built up around MongoDB. MongoDB Atlas provides the managed services we need to run, scale, secure, and backup our data. How do you see the broader benefits of MongoDB in your business? In the ever-changing AI tech market, MongoDB is our stable anchor. MongoDB provides the freedom to work with structured and unstructured data while using any of our preferred tools, and we leave database management to the Atlas service. This means my developers are free to create with AI while being able to sleep at night! MongoDB is familiar to our developers so we don’t need any DBA or external experts to maintain and run it safely. We can invest those savings back into building great AI-powered products. What are your future plans for new applications and how does MongoDB fit into them? We’re always looking for opportunities to offer new functionality to our users. Capabilities like Atlas Search for faceted full-text navigation over data coupled with MongoDB’s application-driven intelligence for more real-time analytics and insights are all incredibly valuable. Streaming is one area that I’m really excited about. Our application is composed of multiple microservices that are soon to be connected with Kafka for an event-driven architecture. Building on Kafka based messaging, Atlas Stream Processing is another direction we will explore. It will give our services a way of continuously querying, analyzing and reacting to streaming data without having to first land it in the database. This will give our customers even lower latency AI outputs. Everybody WINNs! Wrapping up Orr, thank you for sharing WINN.AI’s story with the community! WINN.AI is part of the MongoDB AI Innovators program , benefiting from access to free Atlas credits and technical expertise. If you are getting started with AI, sign-up for the program and build with MongoDB.
Why Leading Insurer Manulife Ditched SQL For MongoDB
Manulife, one of the largest life insurance companies in the world, is in the midst of a digital transformation. Earlier this year, Harry Cheung, Chief Architect of Manulife Asia, spoke to industry experts and developers at MongoDB.local in Hong Kong, outlining the transformation journey so far and what’s next for Manulife. Better experiences, happier customers Manulife, like many large enterprises, is under pressure to get new digital products to market, fast. In addition, the insurer is constantly looking for ways to better connect with and serve customers, in real time, by broadening their digital capabilities and further personalizing the interactions customers have with Manulife. Manulife’s existing data infrastructure, however, was becoming a drag on innovation. Traditional relational databases limited how fast the Manulife team could bring new digital products to market. In particular, Manulife’s developers, the architects of these new digital products and services, faced issues working with the existing data infrastructure, including the need to constantly optimize the database, deal with data normalization issues, and work with slow querying of data. From Relational to NoSQL to MongoDB From the outset, Manulife knew that they would build their new digital experience on a NoSQL database. NoSQL is core to our strategy of building our digital experience. The flexible data model [for NoSQL] means you’re not limited by the schema. Harry Cheung, Chief Architect, Manulife Asia After deciding to go the NoSQL, Manulife was won over to MongoDB for several reasons, including: The document data model: MongoDB's document data model means no rigid schemas to slow down development. This allows for faster iterations when building new digital products. From on-premises to the cloud: Moving from a MongoDB on-premises deployment to MongoDB Atlas in the cloud was easy for the Manulife team. Scalability: MongoDB can easily scale horizontally to meet spikes in demand. Enterprise-ready & mature: MongoDB is used by the world’s largest insurers, offering greater flexibility alongside the sorts of core requirements you would expect from an RDBMS, such as ACID transactions. MongoDB Support: Assistance with projects like data migration from on-premises to cloud services on MongoDB Atlas made the transition smoother. A pay-as-you-go model: MongoDB’s elastic scaling capabilities and flexible pricing model keep costs down. On and offline functionality: MongoDB Atlas has built-in mobile device synchronization capabilities, speeding up the development of offline-first insurance applications. Built with MongoDB: Four Use Cases for Manulife MOVE, a Health-Focused App: MOVE is a digital app that encourages users to meet fitness goals, with daily steps linked to insurance premium discounts. MongoDB's JSON-based document model simplified app development and data management. Secondly, Manulife started running the MOVE app on-premises. When they wanted to migrate the app to a public cloud of their choice (from MongoDB to MongoDB Atlas) the process was seamless. Sales Assistance App: Used by 90% of agents, this app helps Manulife agents in the field service customers and complete applications. One area where MongoDB Atlas was particularly helpful was mitigating issues with mobile connectivity and data synchronization. Agents in the field often suffer from internet service interruptions, such as a dropped mobile signal. When the agent’s sales app reconnects, the data from the app has to be synchronized with the backend MongoDB database. Building apps that can handle such offline/online data synchronization, also known as offline-first apps, can significantly eat into development time, slowing time to value for organizations developing robust offline-first apps. MongoDB Atlas Device Sync solves this issue with native offline to online synchronization capabilities to enable uninterrupted client interactions, even in low connectivity areas. Using Atlas Device Sync, the sales app can store customer, proposal, application, and document metadata on the local device (using MongoDB’s dedicated mobile device database), and then synchronize that data and the customer application to the main MongoDB database when connected to the internet. Manulife launched their sales app's offline mode in just 2 months with MongoDB Atlas Device Sync Policy Life Cycle Management: Traditional relational databases spread policy data across multiple tables. With MongoDB, a single document can encapsulate an entire policy, streamlining querying access and enhancing performance. MongoDB is now the system of record for policy servicing and life cycle management. This new system was met with overwhelming approval from Manulife’s developers. In the past, we were using a traditional relational database, with more than 500 core tables. With MongoDB, when I asked developers who had previously used our traditional [RDBMS] database, ‘You have a choice, do you want to use MongoDB or go back to the traditional [database]?’ all our developers said MongoDB. Harry Cheung, Chief Architect, Manulife Asia Claims Processing: MongoDB's capability to handle structured and unstructured data simplified integration with partners, especially in Optical Character Recognition (OCR) for claim processes. Looking ahead Manulife is set on expanding its use of NoSQL databases, with MongoDB identified as the go-to solution for such projects. MongoDB is our internal standard. MongoDB is our strategic partner for NoSQL development. Harry Cheung, Chief Architect, Manulife Asia About Manulife Manulife Financial Corporation is one of the largest life insurance companies in the world. The company provides insurance and financial services to millions of customers in Asia, Canada, and the United States. Manulife operates under different brand names: Manulife in North America and Asia, and John Hancock in the U.S. It's recognized for its long-standing presence in Hong Kong, with a focus on life insurance, mutual funds, and other financial products. In addition to life insurance, Manulife offers a wide range of financial services including wealth and asset management, group benefits, and retirement services. Learn more about our work with the world's leading insurers on our MongoDB for Insurance page.
You Asked, We Listened. It's Here - Dark Mode for Atlas is Now Available in Public Preview
We are thrilled to announce a much-anticipated feature for MongoDB Atlas. Dark mode is now available in Public Preview for users worldwide. Dark mode has been the number one requested feature in MongoDB's feedback forum , and we've taken note. Users have tried browser plugins and other makeshift fixes, but now the wait is over. Our development team diligently worked to introduce a dark mode option, improving user experience with a new and refreshing perspective to the familiar interface of Atlas. This update—which includes 300 converted pages—is not just for our community. It also benefits us as developers, promoting a seamless dark mode experience across different tools in the developer workflow. Dark mode is sleek and sophisticated, aligning with the preferred working styles of many of our developers. Remember that this is an ongoing project, and there may be areas within Atlas that need refining. Rest assured, we will be monitoring our feedback channels closely. Not just a sleek interface We took a thoughtful approach to the overall dark mode user experience, particularly with respect to accessibility considerations. We ensured that our dark mode theme met accessibility standards by checking and adjusting all text, illustrations, and UI elements for color and contrast to help reduce eye strain and address those with light sensitivities while making sure it was still easy to read. We also focused on accommodating the overall light-to-dark background contrast while staying mindful of how they may layer or interact with other elements. Beyond aesthetics, dark mode is a proven method for extending battery life. For our users with OLED or AMOLED screens dark mode ensures the device’s battery life stretches even further by illuminating fewer pixels and encouraging lower brightness levels. Health benefits A typical engineer spends no fewer than eight hours a day in front of a computer, exposing their eyes to multiple digital screens, according to data from Medium . This screen usage can lead to dry eyes, insomnia, and headaches. While dark text on a light background is best for legibility purposes, light text on a dark background helps reduce eye strain in low-light conditions. Enable dark mode preview today To update the theme at any time, navigate to the User Menu in the top right corner, then select User Preferences . Under Appearance , there will be three options. Light Mode: This is the default color scheme. Dark Mode: Our new dark theme. Auto (Sync with OS): This setting will match the operating system's setting. A few things to keep in mind This is a user setting and does not impact other users within a project or organization. Dark mode is not currently available for Charts, Documentation, University, or Cloud Manager. Since we are releasing this in Public Preview , there might be some minor visual bugs. The goal of Public Preview releases is to generate interest and gather feedback from early adopters. It is not necessarily feature-complete and does not typically include consulting, SLAs, or technical support obligations. We have conducted comprehensive internal testing, and we did not find anything that prevents users from using Atlas. While we are still making a few finishing touches feel free to share any feedback using this form . Thank you to all our users who provided valuable feedback and waited patiently for this feature! Keep the feedback coming . We hope you enjoy dark mode, designed to improve accessibility, reduce eye strain and fatigue, and enhance readability. We invite you to experience the difference. Try dark mode today through your MongoDB Atlas portal .