Andre Podnozov

AI agents are only as powerful as their memory. Redis has been named the #1 data storage choice for AI agents in 2025. The reason? Redis delivers what AI truly needs: - Real-time vector storage - Semantic caching - Long-term memory for LLMs At Bassirah, we see this as validation of why Redis is central to the next generation of AI-native data platforms powering everything from agentic RAG systems to enterprise decision engines. 🔗 Read more: https://lnkd.in/db47k6zG #AI #Redis #RAG #LLM #DataEngineering #AIAgents #ArtificialIntelligence

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Data is in the air! With back-to-back Snowflake and Databricks conferences in SF, one thing that resonated is that data is paramount for AI. While everyone's talking about data use in AI innovation, securing data in the age of AI couldn't be more critical. This week, our CTO, Priyanka Tembey, dives deep into why runtime protection is essential for securing data in use within modern AI systems. Discover how Operant's AI Gatekeeper simplifies this complex challenge, making robust AI security achievable: https://lnkd.in/gYcbixSB #AIGatekeeper #SecureAI #AgenticAI #SnowflakeSummit #DataAISummit #DatabricksSummit #AIPrivacy #DataInMotion #Runtime #DataCloud #AIReady #DataTransformation #RealTimeAI #Datasecurity

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What happens when you pair LangGraph’s flexibility with Snowflake’s secure, scalable compute? You get a seamless path to production-grade LLM applications. Fabian Hernandez, part of our Infostrux team, walks you through how LangGraph agents can now be executed entirely inside Snowflake using its new LLM function framework. It’s an exciting step forward for anyone building: -LLM agents -Retrieval-augmented generation pipelines -Conversational workflows directly tied to enterprise data This article is a must-read for teams operationalizing AI where it matters most—in the cloud, with governance built in. Explore it here: 👉 https://hubs.ly/Q03ykwlj0 #Snowflake #LLMFunctions #LangGraph #AIWorkflows #GenAI #EnterpriseAI #Infostrux

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We co-authored this new white paper with JBF Consulting exploring what changes when transportation optimization moves beyond CPU-based architectures to GPU-accelerated optimization. One insight stands out clearly: the historic trade-off between optimization runtime, scale, and solution quality isn’t a law of physics - it’s an architectural limitation. In the paper, we go deeper into why CPU-based solvers hit that ceiling, how GPU-accelerated optimization changes the equation, and what that means for real-world, highly constrained transport networks. 👉 Read the full white paper - see the link in the first comment. #SupplyChainTechnology #TransportationOptimization #LogisticsStrategy #GPU #TMS #Chora #Tramm

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We’re proud to partner with Groq to power enterprise AI agents with low-latency inference for performance-critical workloads. Teams on StackAI deploy Groq for latency-sensitive workflows: high-volume document analysis, customer-facing interactions, and internal knowledge retrieval. In each of these situations, fast, reliable responses materially improve user experience and productivity. StackAI and Groq are a prime example of how matching the right inference provider to the right workload helps enterprises build AI agents that perform in real production environments. Want to learn more? Read the full piece at the link in comments.  #StackAI #Groq #EnterpriseAI #AIagents

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Tuning Spark, Ray, or Dask for ML isn’t one-size-fits-all. In a new Domino Blueprint, Principal Solution Architect Sameer Wadkar shows how API-driven scaling complements autoscaling to right-size clusters on demand—cutting idle spend and speeding bursts for GPU/I/O-heavy work. Practical steps, a lightweight Python client, and Helm install included. If you care about FinOps and MLOps, this is for you. Read the blueprint: https://lnkd.in/ga_qJkhC

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Legacy semantic models were never built for the velocity and complexity of AI-driven analytics. They assume fixed schemas, predictable joins, and a handful of BI queries, not hundreds of dynamic, multi-domain requests from copilots and agents. AtScale’s AI-powered modeling engine changes that through an adaptive feedback loop. It continuously analyzes warehouse metadata, query logs, and lineage to infer semantic relationships and hierarchies automatically. When candidate metrics or joins are discovered, they’re surfaced for human validation, accepted, rejected, or refined, and those outcomes feed back into the model’s learning system. All of this runs on AtScale’s composable semantic architecture, governed through Semantic Modeling Language (SML). Every suggestion, update, and approval is version-controlled, reversible, and fully auditable. It’s modeling that scales with your data, not against it. 🔗 Learn more:

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Engineering at Snowflake means solving problems no one has tackled before. For Principal Software Engineer Hyungtae Kim and the Cortex team, that meant designing GPU-powered infrastructure flexible enough to handle any AI workload, at scale. Get a behind-the-scenes look at how the foundation was built, what makes it different, and how it’s powering the next era of GenAI: https://bit.ly/4kKjGVI

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If your team is building with LLMs, you know the value of clean, retrievable data. That's why we're teaming up with Unstructured to streamline one of the hardest parts of AI development. This integration removes friction across the AI stack—from unstructured ingestion to semantic retrieval—so your teams can focus on building. Read more from our Product Marketing Managers, Rini Vasan and Jim Allen Wallace, and Unstructured’s Head of Developer Relations, Maria Khalusova here: https://lnkd.in/e98QqXAB

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New research from Databricks. It's about training enterprise search agents via RL. KARL introduces a multi-task RL approach where agents are trained across heterogeneous search behaviors, constraint-driven entity search, cross-document synthesis, and tabular reasoning. It generalizes substantially better than those optimized for any single benchmark. KARL is Pareto-optimal on both cost-quality and latency-quality trade-offs compared to Claude 4.6 and GPT 5.2. With sufficient test-time compute, it surpasses the strongest closed models while being more cost efficient.

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🧠 Databricks vs. Keewano: The Future of AI Analytics for Product Teams Not all AI analytics platforms are created equal. While Databricks powers enterprise data infrastructure, Keewano is purpose-built for fast, causal insights in games and mobile apps—with 600× faster access and always-on AI agents that deliver answers, not just dashboards. 🚀 Compare setup effort, cost, speed, and how each handles real-time product optimization at scale. 🔍 Read the full breakdown: https://lnkd.in/dxcNCNKB #Keewano #Databricks #AIAnalytics #ProductIntelligence #GameAnalytics #LLM #AIAgents #Retention #Churn #DataPlatform #TechComparison #BuildBetterGames

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The AI Forward Deployed Engineer is here. Amplify your development work with AI labor, and reach your next breakthrough faster than ever. At DevCon 3, watch as Palantir Software Engineer Ankit Shankar unveils the AI Forward Deployed Engineer — an agentic system that can build data transformations, create and edit your ontology, create functions, build entire applications, and much more.

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The Databricks Startups team answers founders’ most common AI questions—where to start without hype, how to stay compliant with sensitive data, what to build versus buy, how to cut hallucinations, and how to keep costs in check. 💡 Get step‑by‑step starting points, governance patterns for sensitive data, a pragmatic build‑versus‑buy lens, tactics to curb hallucinations, and cost controls that scale. 🔗 Full post: https://lnkd.in/ehRa3Yp5 #Databricks #DatabricksForStartups #AI #GenAI #MLOps

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