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  <title>Sema — Notes on trustworthy AI analytics</title>
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  <description>Practical writing on semantic layers, governed text-to-SQL and AI data governance — from the team building Sema.</description>
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  <lastBuildDate>Thu, 30 Jul 2026 11:33:14 GMT</lastBuildDate>
  <item>
    <title>Why Data Teams Are Switching to a Governed Semantic Layer</title>
    <link>https://semalayer.com/blog/why-teams-choose-a-semantic-layer</link>
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    <pubDate>Fri, 24 Jul 2026 00:00:00 GMT</pubDate>
    <description>The concrete benefits of putting a governed semantic layer between people and data: one source of truth, self-serve answers everyone trusts, and analysts freed from the ad-hoc queue.</description>
    <category>Product</category>
    <author>Sema Team</author>
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    <title>The ROI of a Semantic Layer: What Governed AI Analytics Actually Saves</title>
    <link>https://semalayer.com/blog/roi-of-a-semantic-layer</link>
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    <pubDate>Sat, 25 Jul 2026 00:00:00 GMT</pubDate>
    <description>A practical model for the return on a governed semantic layer — analyst hours reclaimed, tool sprawl collapsed, faster decisions, and audit prep measured in hours instead of weeks.</description>
    <category>ROI</category>
    <author>Sema Team</author>
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  <item>
    <title>Governance That Scales: Making AI Analytics Audit-Ready by Default</title>
    <link>https://semalayer.com/blog/governance-that-scales-ai-analytics</link>
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    <pubDate>Mon, 27 Jul 2026 00:00:00 GMT</pubDate>
    <description>Governance can't be a review step at the end. Here's how to make AI analytics audit-ready by construction — identity-aware queries, masking, signed evidence, and an append-only audit log.</description>
    <category>Governance</category>
    <author>Sema Team</author>
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  <item>
    <title>Inside Sema: The Architecture Behind a Trustworthy Answer</title>
    <link>https://semalayer.com/blog/inside-sema-architecture</link>
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    <pubDate>Tue, 28 Jul 2026 00:00:00 GMT</pubDate>
    <description>A look under the hood at the six layers that turn a plain-English question into a governed, signed answer — semantic graph, grounded planning, policy engine, sandboxed execution, auto-viz, and evidence.</description>
    <category>Technology</category>
    <author>Sema Team</author>
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  <item>
    <title>Beyond Chat-with-Your-Data: What an AI Analyst Actually Does</title>
    <link>https://semalayer.com/blog/beyond-chat-with-your-data</link>
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    <pubDate>Wed, 29 Jul 2026 00:00:00 GMT</pubDate>
    <description>A chatbot prints a table. An AI analyst plans the analysis, picks the right chart, assembles a narrative from real numbers, enforces governance, and hands you a shareable deck. Here's the difference.</description>
    <category>Product</category>
    <author>Sema Team</author>
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    <title>The Semantic Layer: The Complete Guide for Data Teams (2026)</title>
    <link>https://semalayer.com/semantic-layer</link>
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    <pubDate>Mon, 08 Jun 2026 00:00:00 GMT</pubDate>
    <description>What a semantic layer is, why AI made it essential, how the architecture works, how to evaluate tools, and how to roll one out — the complete 2026 guide.</description>
    <category>Pillar guide</category>
    <author>Sema Team</author>
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  <item>
    <title>What Is a Semantic Layer? A Practical Definition for Data Teams</title>
    <link>https://semalayer.com/blog/what-is-a-semantic-layer</link>
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    <pubDate>Wed, 10 Jun 2026 00:00:00 GMT</pubDate>
    <description>A semantic layer translates raw schema into business meaning — entities, metrics, glossary and policy. Here's a practical definition, examples, and why AI made it essential.</description>
    <category>Semantic layer</category>
    <author>Sema Team</author>
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  <item>
    <title>Semantic Layer vs Metrics Layer vs Data Catalog: What's the Difference?</title>
    <link>https://semalayer.com/blog/semantic-layer-vs-metrics-layer-vs-catalog</link>
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    <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
    <description>Three overlapping categories, three different jobs. How semantic layers, metrics layers and data catalogs differ, where they overlap, and which one your team actually needs.</description>
    <category>Semantic layer</category>
    <author>Sema Team</author>
  </item>
  <item>
    <title>Why LLMs Hallucinate on Analytics Questions — and How Grounding Fixes It</title>
    <link>https://semalayer.com/blog/why-llms-hallucinate-on-data</link>
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    <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
    <description>Text-to-SQL models don't fail on syntax — they fail on meaning. The five failure modes behind AI analytics hallucinations, and the grounding architecture that eliminates them.</description>
    <category>AI analytics</category>
    <author>Sema Team</author>
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    <title>Text-to-SQL Governance: How to Let AI Query Production Data Safely</title>
    <link>https://semalayer.com/blog/text-to-sql-governance</link>
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    <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
    <description>AI-generated SQL needs guardrails humans never did: query-time access control, column masking, audit logs and refusal behavior. A practical governance model for text-to-SQL.</description>
    <category>Governance</category>
    <author>Sema Team</author>
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  <item>
    <title>AI Data Governance: A Practical Framework for Data Teams</title>
    <link>https://semalayer.com/blog/ai-data-governance-framework</link>
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    <pubDate>Tue, 23 Jun 2026 00:00:00 GMT</pubDate>
    <description>AI systems now read, query and summarize enterprise data. A practical six-pillar governance framework covering classification, access, grounding, audit, quality and accountability.</description>
    <category>Governance</category>
    <author>Sema Team</author>
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  <item>
    <title>Why Self-Service Analytics Keeps Failing — and What Actually Works</title>
    <link>https://semalayer.com/blog/why-self-service-analytics-fails</link>
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    <pubDate>Fri, 26 Jun 2026 00:00:00 GMT</pubDate>
    <description>Three generations of self-service BI promised to free the data team. Each failed the same way: tools without shared meaning. Here's the trust equation that determines whether self-serve sticks.</description>
    <category>AI analytics</category>
    <author>Sema Team</author>
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  <item>
    <title>How to Build a Business Glossary Your Team Actually Uses</title>
    <link>https://semalayer.com/blog/business-glossary-guide</link>
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    <pubDate>Mon, 29 Jun 2026 00:00:00 GMT</pubDate>
    <description>Most business glossaries die as wiki graveyards. The ones that survive are executable — wired into the query path. A practical guide to building and maintaining a glossary that stays alive.</description>
    <category>Semantic layer</category>
    <author>Sema Team</author>
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  <item>
    <title>PII, Masking and Access Control for AI Analytics</title>
    <link>https://semalayer.com/blog/pii-masking-ai-analytics</link>
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    <pubDate>Thu, 02 Jul 2026 00:00:00 GMT</pubDate>
    <description>How to keep PII out of LLM context and AI answers: automatic classification, role-based masking, refusal behavior and audit — with concrete patterns for healthcare, finance and SaaS.</description>
    <category>Governance</category>
    <author>Sema Team</author>
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  <item>
    <title>Sema vs dbt Semantic Layer: Which Semantic Layer Do You Need?</title>
    <link>https://semalayer.com/compare/sema-vs-dbt-semantic-layer</link>
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    <pubDate>Fri, 03 Jul 2026 00:00:00 GMT</pubDate>
    <description>dbt's Semantic Layer serves code-defined metrics to BI tools. Sema builds a governed semantic layer automatically and answers plain-English questions. An honest comparison of both.</description>
    <category>Comparison</category>
    <author>Sema Team</author>
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  <item>
    <title>Sema vs Cube: Semantic Layer for AI vs Headless BI</title>
    <link>https://semalayer.com/compare/sema-vs-cube</link>
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    <pubDate>Sat, 04 Jul 2026 00:00:00 GMT</pubDate>
    <description>Cube is a developer-focused headless BI platform with caching and APIs. Sema is an AI-native semantic layer for governed plain-English analytics. Which fits your team?</description>
    <category>Comparison</category>
    <author>Sema Team</author>
  </item>
  <item>
    <title>Sema vs AtScale: Enterprise OLAP Semantic Layer vs AI-Native</title>
    <link>https://semalayer.com/compare/sema-vs-atscale</link>
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    <pubDate>Sun, 05 Jul 2026 00:00:00 GMT</pubDate>
    <description>AtScale brings OLAP-style semantic modeling and query virtualization to enterprise BI. Sema brings governed plain-English analytics. Here's how to choose between them.</description>
    <category>Comparison</category>
    <author>Sema Team</author>
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  <item>
    <title>Sema vs Looker: LookML's Semantic Model vs an AI-Native Layer</title>
    <link>https://semalayer.com/compare/sema-vs-looker</link>
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    <pubDate>Mon, 06 Jul 2026 00:00:00 GMT</pubDate>
    <description>Looker pioneered the in-BI semantic model with LookML. Sema decouples the semantic layer from BI and makes it AI-native. An honest comparison for teams choosing in 2026.</description>
    <category>Comparison</category>
    <author>Sema Team</author>
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