Customer Service Expert Wati.io Scales Up on MongoDB

Nick Bell
November 25, 2024

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.

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Staff Engineering at MongoDB: Your Path to Making Broad Impact

Andrew Whitaker is a Senior Staff Engineer at MongoDB. His previous experience spans tiny startups to enormous organizations like AWS, where he held several different roles focusing on databases. Before joining MongoDB, he worked at a startup building optimized machine learning models in the cloud. Read on to learn more about why Andrew decided to join MongoDB in a senior-level engineering role and how his work is driving improvement within our engineering organization. Why MongoDB I have long been a fan of MongoDB’s products and services. MongoDB the database has always been a pleasure to work with – the system “brings joy” to quote a phrase. As a Python developer, I appreciate how the Python driver feels “Pythonic” in a completely natural way. The programmer interacts with the database using Python constructs: dictionaries, lists, and primitive types. By contrast, SQL databases force me to change my mental model, and the query language feels like an add-on that does not blend with the core language. As an engineer, I am always looking to expand my knowledge and grow my skills. The scope of challenges engineers face at MongoDB is what triggered my interest in the company. We obviously have people working on core databases and distributed systems. But, we also have teams dedicated to machine learning, streaming data, analytics, networking, developer tooling, drivers, and many more areas. It is very hard to get bored working at MongoDB. Finally, I would be remiss if I did not mention the people. Overall, MongoDB’s engineering culture prioritizes intelligence, low ego, and an ability to get stuff done. CL/CI (Continuous Learning, Continuous Improvement) Working at MongoDB has provided me with opportunities for continued learning and growth. Though I do not program as much as I did earlier in my career, I have recently been exploring the Rust language. I’m excited by Rust because it avoids the tradeoffs between predictable performance and safety. My work in the search space has given me exposure to the fast moving world of AI: vector embeddings, RAG, etc. For various reasons, I think MongoDB is uniquely positioned to do well in this area. On top of this, I’m working on some initiatives that are not fully public. I can say that one focus area is improving the sharding experience for our customers. We believe MongoDB sharding is best-in-breed. Still, the process requires more manual configuration than we think is ideal: customers select the shard key, cluster type, shard count, etc. We give guidance here, but I think we can raise the bar in terms of offering a seamless experience with less “futz”. I’m also working with the search team. We believe there is a natural affinity between MongoDB’s document model and AI/ML workloads. We have some features in the works that extend this integration in new and interesting ways. I also spend a fair bit of time driving quality improvements across our suite of products. Our CTO Jim Scharf frequently refers to our “ big 4 ” goals: security, durability, availability, and performance. These goals are more important than any feature we build. I’ve been working across the company to help teams define their availability SLO/SLAs. It turns out that measuring availability is a subtle topic. For example, a naive approach of counting the percentage of failed requests can underestimate downtime because customers make fewer requests when a service is unavailable. So, the first step is to clarify the definition of availability. Finally, as a lapsed academic (in a distant life, I was a graduate student at the University of Washington Department of Computer Science and Engineering), I’m always interested in finding ways to bridge theory and practice. I’ve been collaborating with some folks in our research team to drive improvements to our replication protocols. There are theoretical results that suggest it is impossible to simultaneously achieve low latency and strong consistency (“linearizability” in the technical jargon). However, we believe there are intermediate points in the consistency/latency spectrum that have not been fully explored. This work hasn't been made into a product yet, but stay tuned. Flexible working MongoDB is a hybrid company. Like many of our engineers, I work outside the company headquarters in New York City (I live in Seattle). I appreciate MongoDB’s approach to hybrid working and that company leadership, starting with Dev , cares about the well-being of their employees. It seems there are companies that don’t seem to trust their employees to make decisions, such as which days to come into the office, so I’m thankful for the autonomy I receive at MongoDB to work in a way that’s best for me. Remote work has its challenges, but I would say that the benefit for my work/life balance has been transformative. Final thoughts I have found MongoDB engineers demonstrate a strong mix of technical depth, pragmatism, and empathy. I have yet to find the “smart jerk” prototype that seems to exist throughout the tech industry. Overall, I have found MongoDB is open to change and growth at both the team level and the individual level. There is a willingness to evolve and improve that aligns with the company’s values and leadership principles and enables the success of our technology and people. Find out more about MongoDB culture and career opportunities by joining our talent community .

November 20, 2024
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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

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

November 21, 2024