Building AI With MongoDB: Optimizing the Product Lifecycle with Real-Time Customer Data

Mat Keep
December 11, 2023 | Updated: August 8, 2024
#genAI

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 Zelta, 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.

As discussed in his Voice of the Customer interview with Amazon Web Services (AWS), Gozzo talks about how velocity drives all product development at Ada. Velocity is measured both in terms of how quickly the company can ship products and features, and how quickly they can learn and iterate. Running MongoDB Atlas on AWS alongside serverless Lambda functions and LLMs through Amazon Bedrock means Ada delivers its applications scalably with repeatability and high performance.

Getting started

Check out our library of AI case studies to see the range of applications developers are building with MongoDB. Head over to our quick-start guide to get started with Atlas Vector Search today.

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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.

December 11, 2023
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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024