Ambee's AI Environmental Data Revolution: Powered by MongoDB Atlas

MongoDB
July 26, 2023
#Atlas

Ambee, a fast-growing climate tech start-up based in India, is making waves in the world of environmental data with its mission to create a sustainable future. With over 1 million daily active users, Ambee provides proprietary climate and environmental data-as-a-service to empower governments, healthcare organizations, and private companies to make informed decisions about their policies and business strategies. Their comprehensive data encompasses emissions, pollen levels, air quality, soil conditions, and more, all crucial for driving climate action while positively impacting businesses’ bottom lines.

Screenshot of Ambee's pollen and air quality map
Ambee's pollen and air quality map

From the outset, MongoDB Atlas has been at the core of Ambee's database architecture, supporting their AI and ML models. Ambee needed something that could manage a vast and diverse data set. MongoDB's flexible document model proved to be a perfect fit, enabling them to store all their data in one centralized location and operationalize it for various use cases. On average, Ambee adds around 10 to 15GB of data every hour.

A significant advantage of MongoDB for Ambee lies in its ability to handle geospatial data, a critical element for their work. With data sourced from satellites, paid data providers, soil readings, airplanes, proprietary IoT devices, and much more, Ambee relies on MongoDB's geospatial capabilities to provide accurate and granular geographical insights. This precision is one of Ambee's key differentiators, setting them apart in the industry.

Ambee's use of artificial intelligence adds another layer of value to their data services. By running AI models on MongoDB Atlas, they not only deliver data-as-a-service to their clients but also provide intelligent recommendations. Ambee's AI-driven platform, Ambee AutoML, serves as a central repository, enabling developers with limited machine learning expertise to train high-quality models. This democratization of machine learning empowers a broader audience to harness its potential, crucial in Ambee’s aim to fight climate change with data.

The practical application of Ambee's AI and data services is amazing. Ambee's data powers many companies across the Fortune 500, including Boots, Kimberly Clark, and many more, to support a variety of use cases. Be it personalized marketing or digital healthcare, Ambee's datasets have helped businesses worldwide achieve remarkable results.

For instance, Boots, a leading British health and beauty retailer, uses Ambee's data to identify regions where pollen and environmental factors trigger allergies. AI recommendations help allocate resources efficiently, enabling Boots to mitigate the impact of allergies and enhance their bottom line while aiding more individuals in need. Ambee has also made a US pollen and air quality map publicly available for anyone to check.

In another use case, Ambee employs AI models to forecast forest fires and their potential outcomes in the U.S. and Canada, providing organizations with critical warnings to protect lives and property in wildfire-prone areas.

Screenshot of Ambee's forest fire dashboard
Ambee's forest fire dashboard

Ambee's future looks promising as they continue to grow covering more regions and incorporating more data, all of which makes its AI-powered services more powerful. The company's APIs are designed for ease of use, and to be as simple as possible for developers to get started with. This ease of use and extensive documentation is helping drive the popularity of the service. The MongoDB-powered APIs are getting more than 10 million calls every day.

Madhusudhan Anand, CTO Ambee said: "Our work with MongoDB Atlas showcases how we can create a sustainable future by providing easily accessible environmental data. MongoDB's unique capabilities in handling diverse data and geospatial information have been instrumental in our success. Together, we are shaping a greener world."

Screenshot of Ambee's extensive API documentation
Extensive API Documentation

As Ambee's popularity and impact continue to grow, its suite of data-driven products is expanding substantially. The company will soon be launching a number of sophisticated tools and platforms to help businesses take their operations to the next level. The next big piece will be C6—a carbon management and accounting platform. Ambee aims to help companies measure, report, and reduce their digital emissions. This will be followed by a programmatic advertising tool that can run campaigns based on environment triggers.

All of which will be powered by MongoDB Atlas. And to unlock these innovative AI solutions, Ambee's team is looking to take advantage of the full developer data platform. For example, using MongoDB Atlas Federated Queries and Atlas search to make 70TB of exclusive environmental data operational.

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Amplificando las Operaciones de Retail con IA Generativa y Búsqueda Vectorial: El Potencial Inexplorado

En el mundo hipercompetitivo del comercio minorista, los líderes de la industria buscan constantemente nuevas formas de revolucionar la experiencia del cliente y optimizar las operaciones. Ahí es donde entran en juego la IA generativa y la búsqueda vectorial. Ambos ofrecen un potencial transformador en múltiples casos de uso minorista, desde campañas de marketing personalizadas hasta una gestión eficiente del inventario, convirtiéndolos en indispensables para aquellos que buscan mantenerse a la vanguardia de la industria. En este blog, exploremos juntos cómo la IA Generativa y la Búsqueda Vectorial ayudan a los minoristas a abordar ineficiencias y contratiempos en sus operaciones para romper nuevos límites y cómo la plataforma de desarrollo MongoDB Atlas es perfecta para lograrlo. La obsolescencia tradicional Tradicionalmente, los minoristas han confiado en sistemas manuales basados en reglas y modelos predictivos básicos para navegar por sus complejos paisajes. Sin embargo, estos sistemas a menudo no son suficientes para manejar el enorme volumen y diversidad de datos generados en entornos minoristas. Como resultado, la focalización personalizada del cliente, la previsión del inventario y otras operaciones cruciales no solo son complejas sino ineficientes. Las implicaciones directas de sistemas tan complejos e ineficientes resultan en pérdida de ventas, pérdida de ingresos, inventarios con exceso o falta de stock y, lo más importante, oportunidades perdidas para establecer relaciones más profundas con los clientes. Como reacción, algunos minoristas han comenzado a explorar soluciones avanzadas de IA y Machine Learning (ML). Pero integrar estas tecnologías en los sistemas existentes suele ser una tarea titánica. Implica lidiar con silos de datos, entender modelos complejos de IA y invertir significativamente en infraestructura y experiencia. Además, estas soluciones a menudo no ofrecen el retorno de inversión deseado debido a la complejidad de implementarlas, gestionarlas y escalarlas para adaptarse a las necesidades cambiantes. Con el document model y la API unificada, aumentas la resiliencia al futuro de tus aplicaciones Las operaciones de inventario, la experiencia del cliente y el desarrollo de productos son algunas de las áreas en las que los minoristas pueden aprovechar la IA Generativa y la Búsqueda Vectorial, así que profundicemos en ellas para comprender completamente los desafíos y oportunidades. La búsqueda de la excelencia operacional Todo comienza con la evolución de tu Gestión de Inventario, de modo que constituya la capa nuclear sobre la cual construir los modelos de IA Generativa para analizar y categorizar grandes cantidades de datos de productos en tiempo real, facilitando la previsión eficiente del inventario. Esto puede ayudar a los minoristas a predecir con precisión la demanda y evitar escenarios de sobreabastecimiento o desabastecimiento. Permitiría una eficiencia operativa en todos los niveles de la cadena de suministro. Imagina tus operaciones de Back of House (BHO) y Front of House (FHO) funcionando en tiempo real, con datos mejorados por IA fluyendo a través de la cadena con aplicaciones Offline First. Te permitiría entender cómo fluyen tus clientes a través de cada canal (ofreciendo verdaderaomnicanalidad), cómo interactúan con tus productos y usar esos datos para crear nuevos flujos de ingresos utilizando modelos LLM para entender cosas como los artículos que se compran comúnmente juntos para mejorar la mercadotecnia visual en tienda y digital, activando la reposición automática inteligente en la cadena de suministro y ofreciendo nuevas formas de añadir contexto a la búsqueda del usuario. Interacciones cliente-marca reimaginadas Abordar los desafíos de gestionar tu catálogo de productos de manera eficiente significa que más adelante será más fácil optimizar experiencias basadas en recomendaciones de productos en tiempo real, campañas de marketing personalizadas y soporte al cliente inteligente. Los modelos de IA Generativa requieren grandes cantidades de datos de entrenamiento de alta calidad para generar salidas significativas y precisas. Si los datos de entrenamiento están sesgados, incompletos o son de baja calidad, los resultados pueden ser poco fiables. La flexibilidad del modelo de documentos MongoDB Atlas combinado con Atlas Device Sync es la base perfecta para usar como solución principal para construir tus modelos de recomendación o tus aplicaciones de experiencia del cliente centralizadas. Usar MongoDB Atlas como tu capa de datos central garantiza que tus modelos de IA Generativa serán alimentados con los datos correctos en tiempo real, y además crear una capa de inteligencia para tus aplicaciones. Añade algo de Atlas Vector Search a la arquitectura de tu aplicación para que maneje grandes volúmenes de datos de manera eficiente, buscando rápidamente a través de espacios vectoriales de alta dimensión, lo que puede acelerar la recuperación de datos de entrenamiento y la generación de diferentes tipos de contenido con IA generativa, con una precisión mejorada que permite la búsqueda semántica encontrando los puntos de datos más similares en el conjunto de entrenamiento para cualquier prompt, que podría ser texto, imágenes o videos. Alimentar los LLM con tus propios datos dinámicos evita que "alucinen", mejorando tu experiencia de búsqueda y el soporte al cliente. Como resultado, puedes evolucionar las experiencias de marca de tus clientes mejorando los modelos de recomendación de productos y los esfuerzos de soporte al cliente, proporcionando soluciones precisas que resuenen con las búsquedas de los clientes, incluso con entradas vagas o parciales. Extendiendo la experiencia de compra mediante la personalización, ayudando a los clientes a navegar a través de catálogos de productos y hacer selecciones basadas en sus preferencias y necesidades, permitiendo a los clientes buscar productos usando imágenes, clave en sectores como la moda o la decoración del hogar. Otro caso de uso valioso sería utilizar modelos LLM para habilitar el análisis de sentimiento en opiniones de clientes, comentarios en redes sociales y otras formas de retroalimentación del cliente para determinar el sentimiento general hacia un producto, marca o servicio, proporcionando valiosos insights para los equipos de marketing y desarrollo de productos. Desarrollo y marketing de productos simplificados Sin una comprensión clara de lo que el cliente necesita o quiere, un producto puede no encontrar mercado. La investigación de mercado y el compromiso del cliente son cruciales para el desarrollo exitoso del producto. En mercados saturados, destacar entre los competidores puede ser un desafío. Las propuestas de valor únicas y las características innovadoras son esenciales para distinguir un producto. La velocidad es esencial en el paisaje de mercado de hoy en día. Los retrasos en el desarrollo de productos pueden llevar a oportunidades perdidas, especialmente al tratar con tendencias o avances tecnológicos. La IA Generativa puede analizar grandes cantidades de datos de clientes e identificar tendencias, preferencias y necesidades. Al generar ideas a partir de estos datos, puede ayudar a desarrollar productos que se adapten mejor a las necesidades de los clientes, creando oportunidades para la venta cruzada o la venta adicional mediante recomendaciones de productos. La IA Generativa también puede extender ciertos esfuerzos de marketing de productos mejorando su fase de diseño y marketing de contenido. Enriqueciendo la generación de contenidos para productos específicos con contexto generado desde tu Vista de Cliente 360 y datos de Front-of-house y Back-of-house, los minoristas podrían crear bucles de crecimiento automatizados para sus líneas de productos en diferentes canales maximizando los ingresos sin sacrificar grandes cantidades de recursos. Conectando los datos de sus canales de marketing con sus datos de contexto empresarial a través de la API unificada de MongoDB Atlas, un servicio de middleware totalmente gestionado, y emparejándolo con Atlas Vector Search, cualquier minorista puede maximizar el ROI de la fase de promoción de su estrategia de entrada al mercado, impulsando sus esfuerzos de marketing de una manera verdaderamente data-driven. El crecimiento automatizado por la IA sólo es posible reduciendo la fricción entre los diferentes silos de datos para facilitar su flujo Si quieres saber más sobre cómo construir búsqueda semántica usando MongoDB Atlas y OpenAI no te pierdas nuestra guía sobre Cómo hacer búsqueda semántica en MongoDB usando Atlas Vector Search .

July 21, 2023
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The Developers' Developers: Two Australian Developers Share Their Connections to Customers

The world’s 28 million software developers are writing the foundations of our future, propelling innovation for their organizations through lines of code by creating game-changing new apps. Indeed, the US Bureau of Labor Statistics predicts that between 2022 and 2032, the number of software developers, quality assurance analysts, and testers will grow 24%, “much faster than the average for all occupations.” Fueling this innovative workforce is another group of developers, the people working behind the scenes to build the tools, technologies and platforms that other developers need to be successful: the developers’ developers. Many developers at MongoDB—which after all was built by developers for developers, and is beloved by enterprises and startups alike—fall into this camp. To learn more about what makes these developers tick, we talked with two Australia-based senior software engineers at MongoDB who love to code for their peers. For Lavender Chan and Angus Lee, there’s nothing like seeing the ripple effect of the code they have been working on and the impact it has on their customers. What’s more, the opportunity to be a “developer’s developer” has allowed Chan and Lee to find a space for deep technical work while thriving in an autonomous environment. At MongoDB we believe developers will build the future. First, can you share more about your roles and what you’re working on? Lavender Chan (LC): I work on the Relational Migrator tool, which allows developers to migrate SQL data onto MongoDB. I joined the company two and a half years ago, and have been part of the Sydney technology scene for the last 10 years. The appeal of joining MongoDB was that it’s a large global company, but in the engineering team you are able to have a big impact and a lot of autonomy. Relational Migrator was a greenfield project, and our team has been able to take the original product idea built out of the US and run with it. I’m a full stack developer and have touched on every feature of the tool. A lot of the engineers were able to contribute and work on new ideas. There’s also a strong emphasis on culture here which practically means a lot of the people I work with are excited to be here and passionate about their roles. Angus Lee (AL): I work in the MongoDB Charts team in Australia and think our team is a sweet spot for developers. I’ve interned for other tech companies and started my career at MongoDB. Since then I’ve been given responsibilities where I can create a lot of impact. My role at MongoDB in Sydney has also given me great opportunities to connect directly to the developers we are creating products for in a way that pushes my work to a higher level. Your roles are focused on products targeted to other developers. How does developing for developers affect your approach to your work? LC: In our roles we are creating directly for other developers, so the work that I am doing is deeply technical and specific. As Migrator is a newer product, we are able to interact directly with our customers—other developers—and often a lot of their questions are quite complex and specific, which means I go on a learning journey in debunking and fixing their problems. AL: We have a strong team culture in that as developers we want to be our own users. That means we want to use other MongoDB team products, and they use ours, so we can better identify pain points and issues for our customers. There’s a term that developers use called “dogfooding” that really sums up how we think on this, which basically means to use your own product. It means for me that I think about writing clean code to help any other developers extend on this, and how effective what I do will be for the user. What I’ve also learnt is how our product helps other products thrive. We should have done all the hard work to transform data and show it through data visualizsation tools so it’s easy for the customer. Can you tell us more about this connection to customers and how MongoDB empowers developers? LC: When Relational Migrator was released as a general product, I went to MongoDB World to work at the booth, and I talked to the developers and customers using the platform. As an engineer, it was an amazing experience and opportunity to see how it was being used and what else we could be doing. This connection of engineers with customers, as well as the ability to speak to them regularly in my role, is unique. In other companies I would need to go through support teams, to go through someone else just to push out a bug fix. Our team is very customer focused, so we can prioritizse features that our customers want. AL: One of the best moments for me at MongoDB was when I went to MongoDB World and I sat down with a customer to talk through a feature of Charts. It was a pivotal moment to see the improvements it makes for the businesses that use it, and the impact it generates for their customers beyond that. I could sit back and see the ripple effect of the code I’m writing. There is also a great feedback engine where our users can submit ideas and other users can vote for that feature. The product managers pick from these and push out features that are directly relevant to the developers using it. Where we really connect is in our aim to create an open forum for developers and customers to provide feedback and suggest ideas. Developers are problem solvers. As part of the MongoDB Love Your Developers campaign, we believe in championing the voice of developers and giving them the freedom to experiment and innovate. How do you see this in action? LC: In other places I was a small cog in a massive system. At MongoDB, I really have an impact, and can see directly how my work translates to our final product. In Sydney, we’re a satellite office, but it’s indicative of our company culture that there is huge trust placed in these teams. We’re given high impact projects and can run with them, which means I’ve been able to watch the Relational Migrator product go from a tiny product used by only a few customers, to one that is now generally available. Not many engineers get to work for a well-established,large company and still have the opportunity to work on and release products like a startup. There is a strong global interest in AI-driven innovations. How have your connections to customers led to innovations in this area? AL: We’ve been able to take an idea for a new AI feature, Natural Language Charts , and take it from concept to being released as a feature at MongoDB.local in London. We could see from our conversations with customers, as well as broader industry trends, that there was strong interest in new AI features so we were able to prioritizse it for Charts. We started with nothing and were given the freedom to research how this feature could work using AI, create a new proof of concept, and from there we were able to push it out into a feature which was a really proud moment. Having this agility and flexibility to prioritize something new is exactly what we want to provide to our customers. I never feel like I'm just churning out code. We are connected to the work and to our customers. MongoDB is built by developers, for developers. Become part of the team changing the way the world works with data!

May 9, 2024