AI Applications In Live Data Environments

🚀 **The enterprise search revolution is happening NOW, and it's not what you think.** For years, we've been building bigger indexes, crawling more data, and duplicating information across systems. But what if I told you the future isn't about hoarding data—it's about bringing AI to the data, dynamically? That's how we think about search at Moveworks. https://lnkd.in/gquPY-Km **Enter Agentic RAG: The paradigm shift that's changing everything.** Instead of static indexes that go stale the moment they're built, imagine an intelligent agent that: ✨ Fetches data in real-time from source systems ✨ Understands context and decides which sources to query ✨ Respects live permissions and security boundaries ✨ Preserves the rich structure of your data **Why this matters:** 🔒 **Security**: No more data duplication nightmares or permission lag ⚡ **Freshness**: Always current information, never stale results 🎯 **Precision**: Context-aware search that adapts to each system's unique structure 📈 **Scale**: Connect to hundreds of apps without massive infrastructure The old way: "Let's copy all the data and hope we can keep it secure and current." The new way: "Let's bring intelligence to where the data lives." Companies like OpenAI and Microsoft are already proving this works. ChatGPT connects to Google Drive and SharePoint in real-time. **This isn't just a technical upgrade—it's a fundamental rethinking of how we approach enterprise knowledge.** The future belongs to organizations that can access their collective intelligence instantly, securely, and contextually. The question isn't whether this shift will happen—it's whether you'll lead it or follow it. What's your take? Are you ready to move beyond the index? #AI #EnterpriseSearch #AgenticRAG #FutureOfWork #Innovation #DataStrategy

Let’s face it—traditional knowledge bases feel like relics in a world that changes by the second. I’ve been searching for something more dynamic, and I think I’ve finally found it. Graphiti: an open-source framework that redefines AI memory through real-time, bi-temporal knowledge graphs. Developed by Zep AI (YC W24), Graphiti is engineered to handle the complexities of dynamic data environments, making it a game-changer for AI agents. Key takeaways: 1) Real-time incremental updates: Graphiti processes new data episodes instantly, eliminating the need for batch recomputations. This ensures that your AI agents always have access to the most current information. 2) Bi-temporal data model: It meticulously tracks both the occurrence and ingestion times of events, allowing for precise point-in-time queries. This dual-timeline approach enables a nuanced understanding of how knowledge evolves over time. 3) Hybrid retrieval system: By combining semantic embeddings, keyword search (BM25), and graph traversal, Graphiti delivers low-latency, context-rich responses without relying solely on large language model summarizations. 4) Custom entity definitions: With support for developer-defined entities via Pydantic models, Graphiti offers the flexibility to tailor the knowledge graph to specific domains and applications. 5) Scalability: Designed for enterprise-level demands, Graphiti efficiently manages large datasets through parallel processing, ensuring performance doesn't degrade as data scales. Integration with Zep Memory !!!! Graphiti powers the core of Zep’s memory layer for LLM-powered assistants and agents. This integration allows for the seamless fusion of personal knowledge with dynamic data from various business systems, such as CRMs and billing platforms. The result is AI agents capable of long-term recall and state-based reasoning. Graphiti vs. GraphRAG_______________________________________________ While Microsoft's GraphRAG focuses on static document summarization, Graphiti excels in dynamic data management. It supports continuous, incremental updates and offers a more adaptable and temporally aware approach to knowledge representation. This makes Graphiti particularly suited for applications requiring real-time context and historical accuracy. #AI #KnowledgeGraphs #Graphiti #RealTimeData #Innovation #TechCommunity #OpenSource #AIDevelopment #DataScience #MachineLearning #Ontology #ZapAI #Microsoft #AdaptiveAI

BREAKING: AI agents can now access the live web, unblocked, structured, and at scale. I’m here at AI4 and just witnessed Bright Data launch The Web MCP, a free infrastructure layer that finally solves one of the biggest roadblocks for agentic AI: reliable, real-time web access. Until now, most AI agents struggled when faced with CAPTCHAs, geo-restrictions, and dynamic sites. The Web MCP changes that by giving agents the ability to: • Pull fresh data instantly • Bypass bot defenses and geo-fencing • Automate browser actions on complex sites • Return structured, ready-to-use JSON results It integrates with all major LLMs, supports frameworks like LangChain, LlamaIndex, and CrewAI, and works out-of-the-box for both locally hosted and cloud-based models. Bright Data is making this available with a free tier of 5,000 monthly requests, opening up possibilities for real-time use cases like travel booking, competitor monitoring, healthcare research aggregation, and social sentiment tracking. This launch could be a turning point for building AI agents that truly interact with the live web—without the friction we’ve seen until now. Learn more here: https://lnkd.in/gDDmWA7C #data #ai #publicdata #brightdata #theravitshow

🚀 Big AI updates from Current Bengaluru today! Apache Flink is getting some major upgrades in Confluent Cloud that make real-time AI way easier: 🔹 Run AI models directly in Flink –Bring your model and start making predictions in real time. No need to host externally. 🔹 Search across vector databases – Easily pull in data from places like Pinecone, Weaviate, and Elasticsearch as well as your real-time streams. 🔹 Built-in AI functions – Flink now has built-in tools for forecasting and anomaly detection, so you can spot trends and outliers as the data flows in. Additionally, Tableflow for Iceberg is now GA, and Delta Lake is in early access, making it easier to connect real-time data streams to your AI workflows without managing ETL pipelines. 💡 Why this matters – AI needs fresh, fast data. These updates make it way easier to run models, retrieve data, and build real-time AI apps without stitching together a dozen different tools. Exciting times for AI + streaming! #Current2025 #Confluent #ApacheFlink #AI #RealTimeData #StreamingAI