Introducing AI-Powered Natural Language Mode in Atlas Charts

At MongoDB, our mission is to empower developers to seamlessly and efficiently build modern applications. To that end, we’ve announced a number of tools and improvements to help developers build faster, from AI-powered SQL query conversion in Relational Migrator to the MongoDB Provider for Entity Core Framework.

In the same spirit, today at MongoDB.local Sydney we’re excited to announce the general availability of Natural Language Mode in Atlas Charts. Not only does this release help developers move faster with AI, it also showcases the work of MongoDB’s nimble Sydney engineering team.

Intelligent developer experiences: The next level of productivity

Over the past year, we’ve introduced a range of intelligent developer experiences across our platform, all of which aim to simplify and accelerate development processes. Overall, our goal is to make tools faster, better, and more connected for our developers, and to enable developers to leverage the power of AI to enhance their experience.

As IBM highlights, true developer productivity involves delivering high-quality outputs that satisfy customers but also avoid developer burnout.

By reducing learning curves, saving time, and providing easily accessible insights, intelligent features enable developers to focus on their most important work: building modern applications that solve real-world problems through outputs that are truly worth developers’ time and effort.

Here’s what we’ve introduced:

  • MongoDB Compass—Developers can use natural language to compose everything from simple queries to sophisticated, multi-stage aggregations.

  • MongoDB Relational Migrator—With natural language, developers can convert SQL queries to MongoDB Query API syntax, streamlining migration projects.

  • MongoDB Documentation—An intelligent chatbot, built on top of MongoDB Atlas and Atlas Vector Search, enables lightning-fast information discovery and troubleshooting during software development.

By integrating AI into our most important developer tools, we’re helping developers cut through the noise and focus on creating innovative solutions.

Simplifying data visualization with Natural language Mode

Visualizing data can be an incredibly effective way of gleaning insights from application data, but creating effective visualizations can require specialized knowledge and experience with business intelligence (BI) tools. Atlas Charts was built to level the playing field.

Now, with Natural Language Mode, developers can create visualizations simply by asking questions in plain English. Natural Language Mode reduces technical barriers and makes data visualization accessible to anyone with data in MongoDB Atlas. This means faster chart creation at an advanced scale—all within the Atlas ecosystem.

Since announcing its development in the fall of 2023, we’ve made significant enhancements to Natural Language Mode, including an expanded suite of chart types built for all kinds of data from patient records to financial trading flows. We’ve also improved performance to ensure faster and more accurate chart generation, including the ability to handle more sophisticated prompts, filtering, sorting, binning, and limiting.

In the next few weeks, we will add more chart variation, as well as another upgrade to our model that will increase accuracy and responsiveness by up to 50%.

Natural Language Mode in action

Now let’s walk through a few examples of what using Natural Language Mode in an Atlas Charts looks like:

Generating a chart using Natural Language Mode

With a simple query like "Show me the sales performance by country and product for Q4 FY2023," developers can instantly generate a relevant chart.

Customizing charts in Classic Mode

After generating a chart, developers can pull it into Classic Mode to fine-tune and customize it to fit their dashboard needs.

Scheduling Dashboards

Developers can also schedule their dashboards to be shared via email, ensuring that key stakeholders receive up-to-date insights automatically.

More data, more insights

Customers are already excited about the possibilities of Atlas Charts and Natural Language Mode, from operational analytics to embedded analytics. For example, one of MongoDB’s early customers has been using Natural Language Mode to track server performance across various regions. Business analysts leverage the feature to gain insights into server performance and share these insights internally. They plan to embed these visualizations into their customer portal using the embedding SDK offered by Atlas Charts.

Another customer said: "As a developer with no prior experience in analytics, I was excited to see Natural Language Mode generate a clear value proposition that showcased what the product is capable of. It makes me want to throw more data in the database to get more insights."

So check out Natural Language Mode in Atlas Charts today, and experience firsthand how AI can simplify and accelerate your data visualization workflows.

Try out Natural Language Mode in Atlas Charts to transform your data visualization process. New to Charts? Register for MongoDB Atlas, deploy a cluster, and activate Charts for free.

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Inside MongoDB’s Early Access Programs

In tech, staying ahead of the curve is essential. Tech companies must continually innovate and release new developments to meet the ever-evolving needs of users and global market demands. One successful strategy is the use of Early Access Programs (EAPs) and Preview Features. These initiatives offer unique opportunities for both companies and users, fostering mutually beneficial relationships that drive product excellence. You may have heard them referred to as "Beta Programs," "Pilot Programs," or "Feature Previews," but they all fall under the same category of early user engagement aimed at refining products before general release. So what are Early Access Programs and Preview Features? EAPs and Preview Features allow a select group of users to test and provide feedback on new features before general release. These programs are aimed at loyal customers, power users, or those who’ve shown interest in the company's products. EAPs grant users early access to upcoming features or products still in development. Participants engage closely with the product team, providing valuable insights and feedback that shape the final product. Preview Features are specific features in a product made available for users to try before the official release. Unlike beta testing, which may involve a full product, preview features are often isolated components of a larger system. What are the Benefits of Early Access Programs? They offer numerous advantages for both companies and users. Direct feedback from real users helps identify bugs, usability issues, and areas for improvement that may not be evident during internal testing. This actionable feedback is crucial in enhancing product quality, allowing companies to release more polished and reliable features. This means higher user satisfaction and fewer post-release problems. EAPs also increase customer engagement and appreciation among participants by offering a hands-on exclusive experience to them. This can foster loyalty and strengthen the relationship between the company and its customers. These programs also provide market validation, offering an opportunity to gauge market interest and demand for new features. This enables companies to make data-driven decisions about which features to prioritize and further invest in. By identifying and resolving potential issues early in the development process, EAPs reduce the risk of major failures post-launch and allow for better resource allocation and planning. How does MongoDB approach Early Access Programs? At MongoDB, EAPs are classified into two categories: private preview and public preview. Private preview entails an invite-only white-glove experience for a select few participants who closely test and provide feedback. Public preview implies the feature is available to the public to try, either as downloadable tools or features in Atlas. Key Elements Selective Participation: MongoDB’s EAPs are typically invitation-only, targeting power users, those who’ve shown significant interest in new features, or are part of our MongoDB Champions community. This selective approach ensures that feedback comes from experienced users who can provide valuable insights. Direct Collaboration: Participants engage directly with MongoDB’s product and engineering teams. This direct line of communication allows for real-time feedback, in-depth discussions, and a collaborative approach to feature development. Structured Feedback Collection: We use a variety of methods to collect feedback, including surveys, structured interviews, and feedback forms. This structured approach ensures that feedback is actionable and can be effectively integrated into the development process. Iterative Development: Feedback is used to make iterative improvements to the features being tested. This agile approach allows us to quickly address issues, refine functionalities, and enhance the overall user experience. Transparency and Updates: We maintain open and transparent communication with our EAP participants, providing regular updates on the status of the features, changes made based on their feedback, and future development plans. This transparency fosters trust and keeps participants engaged throughout the program. Rewards and Recognition: Participants can share their stories on our social channels, be part of global events, and win MongoDB swag! Be Part of MongoDB’s Innovation MongoDB’s Early Access Programs offer participants the chance to gain early access to innovative features, influence product development, and join an exclusive, engaged community. As we continue to innovate and expand our product offerings, our Early Access Programs will help us deliver high-quality, user-centric solutions that empower our customers to achieve their strategic objectives. Join us in shaping the future of MongoDB by enrolling in our early access programs today!

July 29, 2024
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Customer Service Expert Wati.io Scales Up on MongoDB

Wati.io is a software-as-a-service (SaaS) platform that empowers businesses to develop conversation-driven strategies to boost growth. Founded by CEO Ken Yeung in 2019, Wati started as a chatbot solution for large enterprises, such as banks and insurance companies. However, over time, Yeung and his team noticed a growing need among small and medium-sized businesses (SMBs) to manage customer conversations more effectively. To address this need, Wati used MongoDB Atlas and built a solution based on the WhatsApp Business API. It enables businesses to manage and personalize conversations with customers, automate responses, improve commerce functions, and enhance customer engagement. Speaking at MongoDB.local Hong Kong in September 2024, Yeung said, “The current solutions on the market today are not good enough. Especially for SMBs [that] don’t have the same level of resources as enterprises to deal with the number of conversations and messages that need to be handled every day.” Supporting scale: From MongoDB Community Edition to MongoDB Atlas “From the beginning, we relied on MongoDB to handle high volumes of messaging data and enable businesses to manage and scale their customer interactions efficiently,” said Yeung. Wati originally used MongoDB Community Edition , as the company saw the benefits of a NoSQL model from the beginning. As the company grew, it realized it needed a scalable infrastructure, so Wati transitioned to MongoDB Atlas. “When we started reaching the 2 billion record threshold, we started having some issues. Our system slowed down, and we were not able to scale it,” said Yeung. Atlas has now become an essential part of Wati’s infrastructure, helping the company store and process millions of messages each month for over 10,000 customers in 165 countries. “Transitioning to a new platform—MongoDB Atlas—seamlessly was critical because our messaging system needs to be on 24/7,” said Yeung. Wati collaborated closely with the MongoDB Professional Services and MongoDB Support teams, and in a few months it was able to rearchitect the deployment and data model for future growth and demand. The work included optimizing Wati’s database by breaking it down into clusters. Wati then focused on extracting connections, such as conversations, and dividing and categorizing data within the clusters—for example, qualifying data as cold or hot based on the read and write frequencies. This architecture underpins the platform’s core features, including automated customer engagement, lead qualification, and sales management. Deepening search capabilities with MongoDB Atlas Search For Wati’s customers, the ability to search through conversation histories and company documents to retrieve valuable information is a key function. This often requires searching through millions of records to rapidly find answers so that they can respond to customers in real-time. By using MongoDB Atlas Search , Wati improved its search capabilities, ultimately helping its business customers perform more advanced analytics and improve their customer service agents’ efficiency and customer reporting. “[MongoDB] Atlas Search is really helpful because we don’t have to do a lot of technical integration, and minimal programming is required,” said Yeung. Looking ahead: Using AI and integrating more channels Wati expects to continue collaborating with MongoDB to add more features to its platform and keep innovating at speed. The company is currently exploring to build more AI capabilities of Wati KnowBot , as well as how it can expand its integration with other conversation platforms and channels such as Instagram and Facebook. To learn more about MongoDB Atlas, visit our product page . To get started with MongoDB Atlas Search, visit the Atlas Search product page .

November 25, 2024