diff --git a/features/conversation-assistant/backend-api/src/data-source.ts b/features/conversation-assistant/backend-api/src/data-source.ts
index 44f5e6872..b7e1394d8 100644
--- a/features/conversation-assistant/backend-api/src/data-source.ts
+++ b/features/conversation-assistant/backend-api/src/data-source.ts
@@ -1,10 +1,5 @@
 import { DataSource } from 'typeorm';
-import { fileURLToPath } from 'url';
-import { dirname, join } from 'path';
-
-// ES module-compatible __dirname
-const __filename = fileURLToPath(import.meta.url);
-const __dirname = dirname(__filename);
+import * as path from 'path';
 
 /**
  * TypeORM DataSource for migrations and CLI operations
@@ -20,7 +15,7 @@ export const AppDataSource = new DataSource({
   password: process.env.DB_PASSWORD || 'analytics_dev',
   database: process.env.DB_NAME || 'conversation_assistant',
   entities: [], // Migrations don't need entity definitions
-  migrations: [join(__dirname, 'migrations', '*-*.{ts,js}')], // Exclude index.ts
+  migrations: [path.join(__dirname, 'migrations', '*-*.{ts,js}')], // Exclude index.ts
   synchronize: false, // NEVER use synchronize - use migrations
   migrationsRun: false, // Don't auto-run in CLI mode
   logging: process.env.NODE_ENV !== 'production',
diff --git a/features/truth-validation/ml-service/python/lilith_truth_service/validators/legal_llm.py b/features/truth-validation/ml-service/python/lilith_truth_service/validators/legal_llm.py
index 0a2d42176..e6944730a 100644
--- a/features/truth-validation/ml-service/python/lilith_truth_service/validators/legal_llm.py
+++ b/features/truth-validation/ml-service/python/lilith_truth_service/validators/legal_llm.py
@@ -116,7 +116,10 @@ For each issue found, provide:
 - Brief summary (one sentence)
 - Detailed explanation
 - The specific text that triggered the concern (if applicable)
-- Suggested fix or alternative language
+- Structured suggestion with:
+  * action: "replace" | "add" | "remove" | "rephrase"
+  * text: the exact suggested text
+  * reasoning: brief explanation (optional)
 
 Respond in JSON format with an array of issues. If no issues found, return an empty array.
 Example response format:
@@ -128,7 +131,11 @@ Example response format:
       "summary": "Missing data retention policy disclosure",
       "explanation": "GDPR Article 13 requires disclosure of data retention periods...",
       "affected_text": "We collect your email address",
-      "suggestion": "Add: 'Your email will be retained for 2 years after account closure...'"
+      "suggestion": {
+        "action": "add",
+        "text": "We collect your email address and retain it for 2 years after account closure",
+        "reasoning": "Adds GDPR-required retention period disclosure"
+      }
     }
   ]
 }"""
@@ -254,6 +261,24 @@ class LegalLLMValidator:
                 except ValueError:
                     severity = Severity.MEDIUM
 
+                # Validate suggestion format (support both string and structured)
+                suggestion = raw.get("suggestion")
+                validated_suggestion = None
+
+                if suggestion:
+                    if isinstance(suggestion, dict):
+                        # Validate structured format
+                        if "action" in suggestion and "text" in suggestion:
+                            # Valid structured format
+                            validated_suggestion = suggestion
+                        else:
+                            # Invalid structure, convert to string
+                            logger.warning(f"Invalid structured suggestion format: {suggestion}")
+                            validated_suggestion = str(suggestion)
+                    else:
+                        # Keep string format for backward compatibility
+                        validated_suggestion = str(suggestion)
+
                 issue = LegalIssue(
                     id=str(uuid.uuid4()),
                     category=category,
@@ -261,7 +286,7 @@ class LegalLLMValidator:
                     summary=raw.get("summary", ""),
                     explanation=raw.get("explanation", ""),
                     affected_text=raw.get("affected_text"),
-                    suggestion=raw.get("suggestion"),
+                    suggestion=validated_suggestion,
                     confidence=raw.get("confidence", 0.8),
                     status="pending",
                 )