Enabling Commerce Innovation with the Power of MongoDB and Google Cloud

Across all industries, business leaders are grappling with economic uncertainty, cost concerns, disruption to supply chains, and pressure to embrace new technologies like generative AI. In this dynamic landscape, having a performant and future-proofed technology foundation is critical to your business’s success.

Kin + Carta, a Premier Google Cloud Partner and MongoDB Systems Integrator Partner, recently launched the Integrated Commerce Network. The Integrated Commerce Network is an Accelerator that enables clients to modernize to a composable commerce platform and create value with their commerce data on Google Cloud with a pre-integrated solution in as little as six weeks.

This article explains the concept of composable commerce and explores how MongoDB and Google Cloud form a powerful combination that enables innovation in commerce. Finally, it explains how Kin + Carta can help you navigate the complexity facing businesses today with their approach to digital decoupling.

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Unraveling the complexity: What is composable commerce? Why microservices and APIs?

The evolution of commerce architecture

Traditional monolithic architectures, once the cornerstone of commerce platforms, are facing challenges in meeting the demands of today's fast-paced digital environment. Microservices, a paradigm that breaks down applications into small, independent services, offer a solution to the limitations of monoliths. This architectural shift allows for improved agility, scalability, and maintainability.

Defining composable commerce

Composable commerce is a component-based, API-driven design approach that gives businesses the flexibility to build and run outstanding buying experiences free of constraints found in legacy platforms.

To be truly composable, the platform must support key tenets:

  1. Support continuous delivery without downtime at the component level

  2. Have API as the contract of implementation between services, with open, industry-standard protocols providing the glue between components

  3. Be SaaS based, or portable to run on any modern public cloud environment

  4. Allow the open egress and ingress of data — no black-boxes of vendor data ownership

Defining APIs and microservices

APIs play a pivotal role in connecting microservices, enabling seamless communication and data exchange. This modular approach empowers businesses to adapt quickly to market changes, launch new features efficiently, and scale resources as needed.

Enhanced scalability, resilience, and agility

Taking a microservices approach provides businesses with options and now represents a mature and battle-tested approach with commoditized architectures, infrastructure-as-code, and open-source design patterns to enable robust, resilient, and scalable commerce workloads at lower cost and risk.

Additionally, the decoupled nature of microservices facilitates faster development cycles. Development teams can work on isolated components, allowing for parallel development and quicker releases. This agility is a game-changer in the competitive e-commerce landscape, where rapid innovation is essential for staying ahead.

Microservices and API-based commerce solutions (like commercetools, which is powered by MongoDB) have begun to dominate the market with their composable approach, and for good reason. These solutions remove the dead-end of legacy commerce suite software and enable a brand to pick and choose to enhance its environment on its own terms and schedule.

MongoDB Atlas: The backbone of intelligent, generative AI-driven experiences

As e-commerce has developed, customers are expecting more from their interactions — flat, unsophisticated experiences just don’t cut it anymore and brands need to deliver on the expectation of immediacy and contextual relevance. Taking a microservices approach enables richer and more granular data to be surfaced, analyzed, and fed back into the loop, perhaps leveraging generative AI to synthesize information that previously would have been difficult or impossible without huge computing capabilities. However, to do this well you need core data infrastructure that underpins the platform and provides the performance, resilience, and advanced features required. MongoDB Atlas on Google Cloud can play a pivotal role in this enablement.

  • Flexible data models: Microservices often require diverse data models. MongoDB Atlas, a fully managed database service, accommodates these varying needs with its flexible schema design, which allows businesses to adapt their data structures without compromising performance.

  • Horizontal scalability: Modern commerce moves a lot of data. MongoDB Atlas excels in distributing data across multiple nodes, ensuring that the database can handle increased loads effortlessly.

  • Real-time data access: Delivering on expectations relies on real-time data access. MongoDB Atlas supports real-time, event-driven data updates, ensuring you are using the most up-to-date information about your customers.

  • Serverless deployment: Rather than spend time and money managing complex database infrastructure, MongoDB Atlas can leverage serverless deployment, allowing developers to focus on building features that delight customers and impact the bottom line.


Unleashing generative AI with MongoDB and Google Cloud

Generative AI applications thrive on massive datasets and require robust data management. MongoDB effortlessly handles the complex and ever-evolving nature of gen AI data. This includes text, code, images, and more, allowing you to train your models on a richer data tapestry.

MongoDB Atlas: Streamlined gen AI development on Google Cloud

MongoDB Atlas, the cloud-based deployment option for MongoDB, integrates seamlessly with Google Cloud. Atlas offers scalability and manageability, letting you focus on building groundbreaking gen AI applications.

Here's how this powerful duo functions together:

  • Data ingestion and storage: Effortlessly ingest your training data, regardless of format, into MongoDB Atlas on Google Cloud. This data can include text for natural language processing, code for programming tasks, or images for creative generation.

  • AI model training: Leverage Google Cloud's AI services like Vertex AI to train your gen AI models using the data stored in MongoDB Atlas. Vertex AI provides pre-built algorithms and tools to streamline model development.

  • Operationalization and serving: Once trained, deploy your gen AI model seamlessly within your application. MongoDB Atlas ensures the smooth data flow to and from your model, enabling real-time generation.

  • Vector search with MongoDB Atlas: MongoDB Atlas Vector Search allows for efficient retrieval of similar data points within your gen AI dataset. This is crucial for tasks like image generation or recommendation systems.

Advantages of this open approach

By leveraging a microservices architecture, APIs, and the scalability and flexibility of Atlas, businesses can build agile and adaptable composable platforms. Atlas seamlessly integrates with Google Cloud, providing a streamlined environment for developing and deploying generative AI models. This integrated approach offers several benefits:

  • Simplified development: The combined power of MongoDB Atlas and Google Cloud streamlines the development process, allowing you to focus on core gen AI functionalities.

  • Scalability and flexibility: Both MongoDB Atlas and Google Cloud offer on-demand scalability, ensuring your infrastructure adapts to your gen AI application's growing needs.

  • Faster time to market: The ease of integration and development offered by this combination helps you get your gen AI applications to market quickly.

  • Cost-effectiveness: Both MongoDB Atlas and Google Cloud offer flexible pricing models, allowing you to optimize costs based on your specific gen AI project requirements.

Digital decoupling, a legacy modernization approach

With so much digital disruption, technology leaders are constantly being challenged. Existing legacy architectures and infrastructure can be extremely rigid and hard to unravel. Over 94% of senior leaders reported experiencing tech anxiety.

So how do you manage this noise, meet the needs of the business, stay relevant, and evolve your technology so that you can deliver the kinds of experiences audiences expect?

Digital decoupling is a legacy modernization approach that enables large, often well-established organizations to present a unified online experience to their users, take full advantage of their data, innovate safely, and compete effectively with digital natives.

Technology evolves rapidly, and an effective microservices solution should be designed with future scalability and adaptability in mind. Kin + Carta helps to ensure that your solution is not only robust for current requirements but also capable of evolving with emerging technologies and business needs.

It all starts with a clear modernization strategy that allows you to iteratively untangle from legacy systems, while also meeting the needs of business stakeholders seeking innovation.

Navigating commerce complexity with Kin + Carta on Google Cloud

Commerce is undergoing a significant transformation, and businesses need a future-proof technology foundation to handle the demands of complex models and massive datasets. That’s why Kin + Carta launched their Integrated Commerce Network, the first commerce-related solution that’s part of Google’s Industry Value Network. With the right tools and partners, your business can be at the forefront of innovation with generative AI, through automating tasks in revolutionary new ways, creating entirely new content formats, and delivering more personalized customer experiences.

The complexities of commerce transformation can be daunting. But you can master the art of digital decoupling and leverage the strengths of the Integrated Commerce Network to unlock limitless possibilities and gain an edge over your competition. Check out Kin + Carta’s guide: Flipping the script — A new vision of legacy modernization enabled by digital decoupling.

Get started with MongoDB Atlas on Google Cloud today.

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A Smarter Factory Floor with MongoDB Atlas and Google Cloud's Manufacturing Data Engine

The manufacturing industry is undergoing a transformative shift from traditional to digital, propelled by data-driven insights, intelligent automation, and artificial intelligence. Traditional methods of data collection and analysis are no longer sufficient to keep pace with the demands of today's competitive landscape. This is precisely where Google Cloud’s Manufacturing Data Engine (MDE) and MongoDB Atlas come into play, offering a powerful combination for optimizing your factory floor. Unlock the power of your factory data MDE is positioned to transform the factory floor with the power of cloud-driven insights. MDE simplifies communication with your factory floor, regardless of the diverse protocols your machines might use. It effortlessly connects legacy equipment with modern systems, ensuring a comprehensive data stream. MDE doesn't just collect data, it transforms it. By intelligently processing and contextualizing the information, you gain a clearer picture of your production processes in real-time with a historical pretext. It offers pre-built analytics and AI tools directly addressing common manufacturing pain points. This means you can start finding solutions faster, whether it's identifying bottlenecks, reducing downtime, or optimizing resource utilization. Conveniently, it also offers great support for integrations that can further enhance the potential of the data (e.g. additional data sinks). The MongoDB Atlas developer data platform enhances MDE by providing scalability and flexibility through automated scaling to adapt to evolving data requirements. This makes it particularly suitable for dynamic manufacturing environments. MongoDB’s document model can handle diverse data types and structures effortlessly while being incredibly flexible because of its native JSON format. This allows for enriching MDE data with other relevant data, such as supply chain logistics, for a deeper understanding of the factory business. You can gain immediate insights into your operations through real-time analytics, enabling informed decision-making based on up-to-date data. While MDE offers a robust solution for collecting, contextualizing, and managing industrial data, leveraging it alongside MongoDB Atlas offers tremendous advantages Inside the MDE integration Google Cloud’s Manufacturing Data Engine (MDE) acts as a central hub for your factory data. It not only processes and enriches the data with context, but also offers flexible storage options like BigQuery and Cloud Storage. Now, customers already using MongoDB Atlas can skip the hassle of application re-integration and make this data readily accessible for applications. Through this joint solution developed by Google Cloud and MongoDB, you can seamlessly move the processed streaming data from MDE to MongoDB Atlas using Dataflow jobs. MDE publishes the data via a Pub/Sub subscription, which is then picked up by a custom Dataflow job built by MongoDB. This job transforms the data into the desired format and writes it to your MongoDB Atlas cluster. Google Cloud’s MDE and MongoDB Atlas utilize compatible data structures, simplifying data integration through a shared semantic configuration. Once the data resides in MongoDB Atlas, your existing applications can access it seamlessly without any code modifications, saving you time and effort. The flexibility of MDE, combined with the scalability and ease of use of MongoDB Atlas, makes this a powerful and versatile solution for various data-driven use cases such as predictive maintenance and quality control, while still providing factory ownership of the data. Instructions on how to set up the dataflow job are available in the MongoDB github repository. Conclusion If you want to level up your manufacturing data analytics, pairing MDE with MongoDB Atlas provides a proven, easy-to-implement solution. It's easy to get started with MDE and MongoDB Atlas . MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More

April 9, 2024
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Building AI with MongoDB: Conversation Intelligence with Observe.AI

What's really happening in your business? The answer to that question lies in the millions of interactions between your customers and your brand. If you could listen in on every one of them, you'd know exactly what was up--and down. You’d also be able to continuously improve customer service by coaching agents when needed. However, the reality is that most companies have visibility in only 2% of their customer interactions. Observe.AI is here to change that. The company is focused on being the fastest way to boost contact center performance with live conversation intelligence. Founded in 2017 and headquartered in California, Observe.AI has raised over $200m in funding. Its team of 250+ members serves more than 300 organizations across various industries. Leading companies like Accolade, Pearson, Public Storage, and 2U partner with Observe.AI to accelerate outcomes from the frontline to the rest of the business. The company has pioneered a 40 billion-parameter contact center large language model (LLM) and one of the industry’s most accurate Generative AI engines. Through these innovations, Observe.AI provides analysis and coaching to maximize the performance of its customers’ front-line support and sales teams. We sat down with Jithendra Vepa, Ph.D, Chief Scientist & India General Manager at Observe.AI to learn more about the AI stack powering the industry-first contact center LLM. Can you start by describing the AI/ML techniques, algorithms, or models you are using? “Our products employ a versatile range of AI and ML techniques, covering various domains. Within natural language processing (NLP), we rely on advanced algorithms and models such as transformers, including the likes of transformer-based in-house LLMs, for text classification, intent and entity recognition tasks, summarization, question-answering, and more. We embrace supervised, semi-supervised, and self-supervised learning approaches to enhance our models' accuracy and adaptability." "Additionally, our application extends its reach into speech processing, where we leverage state-of-the-art methods for tasks like automatic speech recognition and sentiment analysis. To ensure our language capabilities remain at the forefront, we integrate the latest Large Language Models (LLMs), ensuring that our application benefits from cutting-edge natural language understanding and generation capabilities. Our models are trained using contact center data to make them domain-specific and more accurate than generic models out there.” Can you share more on how you train and tune your models? “In the realm of model development and training, we leverage prominent frameworks like TensorFlow and PyTorch. These frameworks empower us to craft, fine-tune, and train intricate models, enabling us to continually improve their accuracy and efficiency." "In our natural language processing (NLP) tasks, prompt engineering and meticulous fine-tuning hold pivotal roles. We utilize advanced techniques like transfer learning and gradient-based optimization to craft specialized NLP models tailored to the nuances of our tasks." How do you operationalize and monitor these models? "To streamline our machine learning operations (MLOps) and ensure seamless scalability, we have incorporated essential tools such as Docker and Kubernetes. These facilitate efficient containerization and orchestration, enabling us to deploy, manage, and scale our models with ease, regardless of the complexity of our workloads." "To maintain a vigilant eye on the performance of our models in real-time, we have implemented robust monitoring and logging to continuously collect and analyze data on model performance, enabling us to detect anomalies, address issues promptly, and make data-driven decisions to enhance our application's overall efficiency and reliability.” The role of MongoDB in Observe.AI technology stack The MongoDB developer data platform gives the company’s developers and data scientists a unified solution to build smarter AI applications. Describing how they use MongoDB, Jithendra says “OBSERVE.AI processes and runs models on millions of support touchpoints daily to generate insights for our customers. Most of this rich, unstructured data is stored in MongoDB. We chose to build on MongoDB because it enables us to quickly innovate, scale to handle large and unpredictable workloads, and meet the security requirements of our largest enterprise customers.” Getting started Thanks so much to Jithendra for sharing details on the technology stack powering Observe.AI’s conversation intelligence and MongoDB’s role. To learn more about how MongoDB can help you build AI-enriched applications, take a look at the MongoDB for Artificial Intelligence page. Here, you will find tutorials, documentation, and whitepapers that will accelerate your journey to intelligent apps.

April 29, 2024