Context, Semantics & RAG
Semantic layers, context engineering and retrieval that make AI answers trustworthy.
Latest on Context, Semantics & RAG.
Context Over Compute: How a Semantic Layer Improves AI ROI
AtScale's Mark Palmer explains why context, not bigger models, drives AI ROI, and how semantic layers and accuracy testing cut errors.
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AI Agents in the Enterprise: Context, Control and Getting to Production
Leaders from Honeywell, Capital One, Synchrony, TelevisaUnivision and EXP Realty share how to give AI agents context, access and oversight.
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What Is Context Engineering? A Field Guide to the AI Context Window
Redis's Jessica Emmons explains context engineering, the four most common context failures, why RAG falls short, and how context engines work.
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The AI Context Engineer: How the Modern Data Team Is Changing
WisdomAI's Geetesh Iyer explains why AI analytics pilots fail at scale, why semantic layers aren't enough, and the rise of the AI context engineer.
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Single Source of Truth for Agentic AI: From Databases to Meaning
SAP, Turgon, Skyflow and AtScale leaders on how agentic AI redefines the single source of truth: semantics, runtime controls and evals on your own data.
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AI Agents Need Context, Not Just Data: Why Data Lakes Fall Short
Teradata's Ben Sanders explains why data lakes built for humans fail AI agents, and the six kinds of context agents need to deliver ROI.
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Enterprise AI Agents: Validation Tax, Context and Scaling Past the Pilot
Tokio Marine, Morgan Stanley and Fedcap leaders on scaling AI agents: the validation tax, human on the loop, enterprise context and when to use workflows.
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AI-Ready Data Governance: Why Business Context Is the New Foundation
Actian, Fiserv and Slalom leaders explain how living business context, automated stewardship and lineage make data governance AI-ready.
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How AI Is Replacing Traditional BI, and Why Context Is the New Job
PromptQL's Adam Malone explains why text-to-SQL and semantic layers stall, and why analysts' future work is teaching AI how the business works.
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Agentic-Ready Data: Why AI Agents Need Metadata, Not Just Clean Data
Snowflake and Precisely experts explain why data built for dashboards fails AI agents, and how metadata, semantic layers and lineage make it agent-ready.
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Self-Service AI Analytics for KYC and AML: Inside Actian AI Analyst
Actian's Phil Wall demos how an AI analyst grounded in governed business context speeds AML and KYC investigations without report requests.
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Agentic AI Demos: Lumi AI, Precisely, SAS Viya and Domino Data Lab
Agentic AI in Action Q2 recap: Lumi AI, Precisely, SAS Viya and Domino Data Lab demo proactive analytics, MCP data enrichment and auditable agents.
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The Context Layer for Enterprise AI: Metadata, Semantics and Memory
Collate, Graphwise, Precisely and AtScale debate the AI context layer: metadata graphs, semantic layers, knowledge graphs, RAG, memory and orchestration.
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How to Build AI Agents With MCP Tools: Teradata and Coder Live Demos
Teradata builds an agent that reasons over policies and tables via governed MCP tools; Coder shares agent harness, sub-agent and context lessons.
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Agent-Ready Data Architecture: Streaming Real-Time Data to AI Agents
Striim and Google Cloud explain why batch warehouses fall short for AI agents and how CDC, event streaming and MCP deliver fresh context on Google Cloud.
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Semantic Intelligence: Why AI Agents Need Always-On Data Governance
Analyst Mike Ferguson and Collate explain why fragmented metadata stalls agentic AI and how unified, agent-assisted governance and knowledge graphs help.
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Agentic AI in Action: Five Live Demos of AI Agents on Enterprise Data
Informatica, PromptQL, Teradata, Precisely and Lumi AI showed AI agents finding, cleaning and analyzing enterprise data in 10-minute live demos.
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Agentic AI in the Enterprise: From Human in the Loop to Agent Ops
Revefi, Precisely, AtScale, Google Cloud and Rhino leaders explain what makes AI agentic, why human in the loop won't scale, and how to govern agents.
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Four Signs Your Data Is Ready for AI: Real-Time, Governed, Event-Driven
Striim and Google Cloud explain four signs of AI-ready data: current state, derived not accessed, preserved business meaning and event-driven design.
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GenAI Demo Day Q4: Snowflake, PromptQL, AtScale, Monte Carlo and More
Snowflake, PromptQL, Google, AtScale, Dataiku, OneTrust, SAS and Monte Carlo demoed AI agents, semantic layers, AI governance and agent observability.
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The Hidden Costs of Poor Data Quality in AI, From Bias to Context
Revefi, Snowplow, Informatica and Fivetran experts explain how poor data quality, missing data and weak context undermine AI accuracy and ROI.
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Agentic AI Showcase: Supermetrics, Striim and Lumi AI Demo Data for Agents
Supermetrics, Striim and Lumi AI demo how AI agents get trusted data: marketing data via MCP, real-time CDC with masking, and an AI data analyst.
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GenAI Demo Day Q3: Snowflake, Denodo, Zenlytic, PromptQL and More
Snowflake, Denodo, Zenlytic, PromptQL, Fivetran, Google Cloud and Precisely demo AI agents, semantic context, A2A and MCP servers in 10 minutes each.
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Multimodal AI Search: How Tensors Make Video Retrieval Work at Scale
Vespa.ai's Bonnie Chase and Zohar Nissare-Houssen explain why vectors aren't enough for multimodal AI and demo hybrid video search with tensors.
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Why Data Democratization Fails, and How Enterprises Close the Gap
AtScale, T-Mobile and Mars data leaders on why self-service data efforts stall, hub-and-spoke governance, data literacy and AI's pressure on readiness.
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Agentic AI Planning, Reasoning and Memory: How to Build Enterprise Agents
Zenlytic, Nexla, Alation and Dataiku leaders define AI agents, explain why memory is unsolved and share how to make agents reliable and scalable.
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Enterprise GenAI Applications: Security, Accuracy and Evaluation at Scale
AtScale, Nexla, Informatica, Vespa and Skyflow leaders explain how to secure data, reduce hallucinations and evaluate enterprise GenAI apps at scale.
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GenAI Demo Day: Snowflake Cortex Agents, TiDB GraphRAG and Zenlytic
Snowflake, TiDB and Zenlytic demo AI agents over enterprise data: Cortex Agents, GraphRAG in a single database and open-ended AI analytics.
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ColPali for Multimodal RAG: Retrieving Charts, Tables and PDFs Visually
Vespa.ai CEO Jon Bratseth and RavenPack's Aakarsh Ramchandani explain how ColPali retrieves PDF pages visually, its limits and how to scale it.
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RAG Use Cases: How UPS, TSYS and Shipt Put Retrieval-Augmented AI to Work
UPS, TSYS and Shipt leaders on retrieval-augmented generation: a digital twin operations copilot, internal use cases, RAG risks and prioritizing GenAI.
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