Docs Added DTE InfoGraphic

Author: brian-brewer

.gitignore

@@ -1,5 +1,4 @@
 # Hugo static site generator
-public/
 resources/_gen/
 .hugo_build.lock
 content/
@@ -85,3 +84,4 @@ evals/artifacts/tmp/*
 dist/
 build/
 .eggs/
+public/

README.md

@@ -34,7 +34,7 @@ This repo hosts the **Manifesto**, **Patterns**, and the **Trust Dashboard** MVP
 
 - 🌐 **Site**: https://datatrustmanifesto.org/
 - 📜 **Manifesto**: [Manifesto.md](Manifesto.md)
-- 🧭 **Patterns**: [docs/patterns/README.md](docs/patterns/README.md)
+- 🧭 **Patterns and Samples**: [docs/patterns/README.md](docs/patterns/README.md)
 - 📊 **Trust Dashboard(s)**: [tools/data-trust-dashboard](tools/data-trust-dashboard)
 
 ## Follow & Join
@@ -48,6 +48,15 @@ This repo hosts the **Manifesto**, **Patterns**, and the **Trust Dashboard** MVP
 
 **Data Trust Engineering (DTE)** is a community-driven approach that empowers data teams to build trusted, AI-ready systems through practical engineering patterns and open-source collaboration. Born from the *Data Trust Engineering Manifesto*, DTE provides actionable frameworks for certifying data systems by use case, risk, and value, blending DataOps principles with hands-on implementation. Our first artifact, the **DTE Trust Dashboard**, demonstrates real-time data and AI trust monitoring—more tools are coming from our community! Join us at [datatrustmanifesto.org](https://datatrustmanifesto.org) and fuel the #DataTrustCommunity.
 
+<div style="text-align: center; margin: 2rem auto; max-width: 1200px;">
+  <img src="assets/DTE-Diagram4.png" 
+       alt="DTE Diagram - Data and Application Modernization" 
+       style="width: 100%; max-width: 1000px; height: auto; border-radius: 8px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
+  <figcaption style="margin-top: 1rem; font-style: italic; color: #666; font-size: 0.9rem;">
+    Data Trust Engineering Data and Application Moderniztion: From data sources through AI assurance with community-driven patterns
+  </figcaption>
+</div>
+
 ## Why DTE?
 
 Traditional data governance often struggles with complexity, conflating compliance requirements with technical data management and creating barriers for engineering teams. Organizations face challenges when AI models encounter data quality issues or when scaling data trust across cloud environments. DTE addresses these practical challenges through: