Simon Eid’s Post

What makes success stories like Observe.AI so exciting for me is witnessing MongoDB's evolution into an AI-accelerating data platform. For an AI-centric company like Observe.AI, choosing the right data architecture was the star. And they recognised MongoDB's distinct strengths to flex across databases, analytics, search, mobile data management and now vector databases for generative AI workloads. Our powerful indexing and querying capabilities allow Observe.AIs data scientists to quickly access and analyse the data powering their AI models. Read how Jithendra Vepa's team is using MongoDB as a core enabler of their innovative AI stack. Jithendra Vepa, Ashish Saxena ,Ashish Gupta,Puneet Tandon , Raunak Bhansali , Sachin Chawla, Shekhar Bharat Singh , Vaibhav Mehra . #MongoDB #artificialintelligence #dataanalytics https://lnkd.in/dTd9Y36w

MongoDB Atlas Vector Search: Transforming Generative AI for Businesses #AI #artificialintelligence #AtlasVectorSearch #CloudServices #dataindexing #GenerativeAI #llm #LLMs #machinelearning #MongoDB #numericalvectors #realtimedataintegration #relevancebasedsearches #VectorDatabases

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.” #mongodb #genai #generativeai #ai #artificialintelligence #machinelearning

Real-time translation, sentiment analysis, and generative AI — all while keeping your data securely within your PostgreSQL instance. https://lnkd.in/gtNx6AfB

🔍💡 Vector databases are having their moment. They also maybe a huge difference in AI solutions. MongoDB Atlas was named one of the top 16 Vector Databases for 2024. Learn why: https://lnkd.in/e_4FAGeU

In the era of #GenAI, proprietary data is more than just an asset—it's a strategic game-changer for businesses. Discover how MongoDB and Cohere's integration capitalizes on the value of frontier generation, embedding and rerank models with advanced retrieval for Enterprise AI. 👇 https://lnkd.in/gCFaHb2k

Very good summary of why MongoDB Atlas is a very great solution for AI use cases and to help developers to be more efficient and building modern applications faster : 1. Boost Information Accuracy for LLMs. 2. Speed Up Data Indexing for Generative AI Applications. 3. Use Real-Time Data Streams. It's all about time to market and architecture simplification.

We woke up this morning to find Generative AI search enabled in MongoDB documentation. It's truly encouraging to see an increasing number of big companies adopting this wonderful technology., The engineer in me, however, couldn't resist drawing a comparison. Below is a short video comparing the two, but please take it with a grain of salt. Contrary to popular belief, building a truly capable search on top of LLMs is challenging. No single algorithm can address all the edge cases, and the accuracy varies for each. GenAI search is just of the product features we are working on at Kwazii Inc. We're delving into explainable analytics, optimizing support agent conversation flows, querying for deep insights, knowledge graph and more. Stay tuned! https://lnkd.in/gU723CWh

Unlock powerful search capabilities with Azure Cosmos DB for #MongoDB vCore! The new vector search feature enhances data exploration, making it easier to manage and search large datasets with advanced AI and machine learning tools. https://lnkd.in/epYVw2kk

As AI models tackle increasingly complex tasks like text-to-image generation, they require more than just keyword matching. They need a true understanding of the fundamental nature of the subjects involved. At MongoDB, we see vectors as a powerful complement to our document data model for fueling AI/ML workloads. Our newly launched vector indexing capabilities in MongoDB Atlas allow developers to easily index their data both by properties and by semantic vector search annotations. This makes it simple to bring AI understanding directly into applications. Especially as Gartner predicts that vector database adoption for generative AI models will surge from just 2% of enterprises in 2023 to 30% by 2026, the tech world is realising the necessity of this shift. #generativeai #artificialintelligence #vector