docs: remove dashes from 'audit ready' for branding consistency

Author: tradertanmay

README.md

@@ -211,7 +211,7 @@ $env:TANML_SERVER_ADDRESS="0.0.0.0"; $env:TANML_PORT="9000"; $env:TANML_MAX_MB="
 
 ## Reports
 
-TanML generates audit-ready reports (.docx) programmatically:
+TanML generates audit ready reports (.docx) programmatically:
 
 * **Model Development Report** — Cross-validation metrics, diagnostics, and performance summary
 * **Model Evaluation Report** — Train/Test comparison, drift analysis, stress testing, SHAP explainability

docs/index.md

@@ -95,7 +95,7 @@ $env:TANML_SERVER_ADDRESS="0.0.0.0"; $env:TANML_PORT="9000"; $env:TANML_MAX_MB="
 
 ## Reports
 
-TanML generates audit-ready reports programmatically:
+TanML generates audit ready reports programmatically:
 
 * **Model Development Report** — Cross-validation metrics, diagnostics, and performance summary
 * **Model Evaluation Report** — Train/Test comparison, drift analysis, stress testing, SHAP explainability

tanml/ui/views/setup.py

@@ -183,7 +183,7 @@ def render_setup_page(run_dir):
             <div class="workflow-card">
                 <div class="icon">📝</div>
                 <h3>Generate Reports</h3>
-                <p>Download audit-ready reports.</p>
+                <p>Download audit ready reports.</p>
             </div>
         </a>
         """,
@@ -227,7 +227,7 @@ def render_setup_page(run_dir):
         </p>
         <p style="margin-bottom: 0;">
             <b>Step 5: Validate Performance (Model Evaluation)</b><br>
-            Once you have selected a model configuration from Step 4, upload your original <i>Training Set</i> alongside your unseen <i>Testing Set</i>. TanML will evaluate your final model onscreen to check how well it generalizes to unseen data, monitor for Data Drift, and generate Explainability (SHAP) plots to help interpret the results. <b>Click 'Generate Evaluation Report' to download a complete, audit-ready document proving your final model's real-world readiness.</b>
+            Once you have selected a model configuration from Step 4, upload your original <i>Training Set</i> alongside your unseen <i>Testing Set</i>. TanML will evaluate your final model onscreen to check how well it generalizes to unseen data, monitor for Data Drift, and generate Explainability (SHAP) plots to help interpret the results. <b>Click 'Generate Evaluation Report' to download a complete, audit ready document proving your final model's real-world readiness.</b>
         </p>
     </div>
     """, unsafe_allow_html=True)