diff --git a/core/services/llm_config.py b/core/services/llm_config.py
index e170e24..4dba1a5 100644
--- a/core/services/llm_config.py
+++ b/core/services/llm_config.py
@@ -32,6 +32,10 @@ class LLMConfig:
             'api_key_env': 'OPENAI_API_KEY',
             'models': ['gpt-4o', 'gpt-4-turbo', 'gpt-4', 'gpt-3.5-turbo']
         },
+        'google': {
+            'api_key_env': 'GOOGLE_API_KEY',
+            'models': ['gemini-2.0-flash', 'gemini-2.5-pro', 'gemini-1.5-pro']
+        },
         'anthropic': {
             'api_key_env': 'ANTHROPIC_API_KEY',
             'models': ['claude-3-opus-20240229', 'claude-3-sonnet-20240229', 'claude-3-haiku-20240307']
@@ -104,6 +108,12 @@ class LLMConfig:
             api_key = os.getenv('ANTHROPIC_API_KEY')
             return Anthropic(api_key=api_key)
 
+        elif provider == 'google':
+            import google.generativeai as genai
+            api_key = os.getenv('GOOGLE_API_KEY')
+            genai.configure(api_key=api_key)
+            return genai
+
         elif provider == 'azure_openai':
             from openai import AzureOpenAI
             api_key = os.getenv('AZURE_OPENAI_API_KEY')
@@ -173,6 +183,10 @@ class LLMConfig:
                     response = cls._call_openai_vision(
                         prompt, image_asset_b64, image_ref_b64, model, debug
                     )
+                elif provider == 'google':
+                    response = cls._call_google_vision(
+                        prompt, image_asset, image_ref, model, debug
+                    )
                 elif provider == 'anthropic':
                     response = cls._call_anthropic_vision(
                         prompt, image_asset_b64, image_ref_b64, model, debug
@@ -250,6 +264,55 @@ class LLMConfig:
             'tokens_used': response.usage.total_tokens if hasattr(response, 'usage') else None
         }
 
+    @classmethod
+    def _call_google_vision(
+        cls,
+        prompt: str,
+        image_asset: Any,
+        image_ref: Any,
+        model: str,
+        debug: bool
+    ) -> Dict[str, Any]:
+        """Call Google Gemini Vision API."""
+        genai = cls.get_client('google', model)
+
+        # Load image as PIL
+        pil_image = None
+        if isinstance(image_asset, Image.Image):
+            pil_image = image_asset
+        elif isinstance(image_asset, str) and os.path.exists(image_asset):
+            pil_image = Image.open(image_asset)
+        elif isinstance(image_asset, bytes):
+            pil_image = Image.open(BytesIO(image_asset))
+
+        if pil_image is None:
+            raise ConfigurationError("Could not load image for Google Vision API")
+
+        # Build content parts
+        contents = [prompt, pil_image]
+
+        # Add reference image if provided
+        if image_ref is not None:
+            ref_image = None
+            if isinstance(image_ref, Image.Image):
+                ref_image = image_ref
+            elif isinstance(image_ref, str) and os.path.exists(image_ref):
+                ref_image = Image.open(image_ref)
+            if ref_image:
+                contents.append(ref_image)
+
+        gen_model = genai.GenerativeModel(model)
+        response = gen_model.generate_content(contents)
+
+        return {
+            'text': response.text,
+            'model': model,
+            'provider': 'google',
+            'tokens_used': getattr(response, 'usage_metadata', {}).total_token_count
+            if hasattr(getattr(response, 'usage_metadata', None), 'total_token_count')
+            else None
+        }
+
     @classmethod
     def _call_anthropic_vision(
         cls,
diff --git a/modules/hm_qc/checks/sample_quality_check.py b/modules/hm_qc/checks/sample_quality_check.py
index e1cdcbc..16209ed 100644
--- a/modules/hm_qc/checks/sample_quality_check.py
+++ b/modules/hm_qc/checks/sample_quality_check.py
@@ -143,12 +143,18 @@ EVALUATION CRITERIA:
    b. Proper exposure (not too dark or too bright)
    c. Consistent white balance
 
-3. Composition and Framing:
+3. Text and Title Legibility:
+   a. All text and titles must be clear, legible, and properly rendered
+   b. No cut-off, overlapping, or unreadable text
+   c. Font sizes must be appropriate and readable at intended display size
+   d. Text must have sufficient contrast against its background
+
+4. Composition and Framing:
    a. Subject properly framed and positioned
    b. Appropriate negative space
    c. No unwanted elements or distractions
 
-4. Professional Standards:
+5. Professional Standards:
    a. Meets commercial photography standards
    b. Suitable for marketing/advertising use
    c. Consistent with H&M brand aesthetics
@@ -163,8 +169,9 @@ SCORING GUIDANCE:
 STEPS TO EVALUATE:
 1. Assess overall image quality and technical aspects
 2. Evaluate color accuracy and lighting
-3. Check composition and framing
-4. Determine if image meets professional standards for H&M marketing
+3. Check that all text and titles are clear and legible
+4. Check composition and framing
+5. Determine if image meets professional standards for H&M marketing
 
 YOUR OUTPUT MUST INCLUDE:
 Format your response as JSON (you can include explanatory text before/after the JSON):
diff --git a/modules/hm_qc/routes.py b/modules/hm_qc/routes.py
index 4d2ab77..fc6c286 100644
--- a/modules/hm_qc/routes.py
+++ b/modules/hm_qc/routes.py
@@ -186,6 +186,7 @@ def execute():
         session_id = data.get('session_id')
         profile_name = data.get('profile')
         job_number = data.get('job_number')
+        llm_provider = data.get('llm_provider')
 
         if not session_id or not profile_name:
             return jsonify({'error': 'Missing required parameters'}), 400
@@ -209,6 +210,14 @@ def execute():
         if not profile:
             return jsonify({'error': f'Profile "{profile_name}" not found'}), 404
 
+        # Override LLM provider if user selected one
+        if llm_provider:
+            provider_models = {'openai': 'gpt-4o', 'google': 'gemini-2.0-flash'}
+            for check in profile.get('checks', []):
+                if check.get('llm_provider'):
+                    check['llm_provider'] = llm_provider
+                    check['llm_model'] = provider_models.get(llm_provider, check.get('llm_model'))
+
         logger.info(f"Starting QC execution for session {session_id} with profile {profile_name}")
 
         executor = QCExecutor(
@@ -262,6 +271,7 @@ def execute_batch():
         session_id = data.get('session_id')
         profile_name = data.get('profile')
         job_number = data.get('job_number')
+        llm_provider = data.get('llm_provider')
 
         if not session_id or not profile_name:
             return jsonify({'error': 'Missing required parameters'}), 400
@@ -283,6 +293,14 @@ def execute_batch():
         if not profile:
             return jsonify({'error': f'Profile "{profile_name}" not found'}), 404
 
+        # Override LLM provider if user selected one
+        if llm_provider:
+            provider_models = {'openai': 'gpt-4o', 'google': 'gemini-2.0-flash'}
+            for check in profile.get('checks', []):
+                if check.get('llm_provider'):
+                    check['llm_provider'] = llm_provider
+                    check['llm_model'] = provider_models.get(llm_provider, check.get('llm_model'))
+
         logger.info(f"Starting batch QC for {len(files)} files (session: {session_id})")
 
         batch_executor = BatchQCExecutor(
diff --git a/modules/hm_qc/templates/hm_qc/configure.html b/modules/hm_qc/templates/hm_qc/configure.html
index fa63067..02dfd25 100644
--- a/modules/hm_qc/templates/hm_qc/configure.html
+++ b/modules/hm_qc/templates/hm_qc/configure.html
@@ -39,6 +39,15 @@
                             {% endfor %}
                         </div>
 
+                        <div class="mb-3">
+                            <label for="llmProvider" class="form-label">AI Provider</label>
+                            <select class="form-select" id="llmProvider">
+                                <option value="openai" selected>OpenAI GPT-4o</option>
+                                <option value="google">Google Gemini</option>
+                            </select>
+                            <div class="form-text">Select which AI model to use for image quality analysis</div>
+                        </div>
+
                         <div class="mb-3">
                             <label for="jobNumber" class="form-label">Job Number (Optional)</label>
                             <input type="text" class="form-control" id="jobNumber"
@@ -89,6 +98,7 @@
 
         const profile = document.getElementById('profile').value;
         const jobNumber = document.getElementById('jobNumber').value;
+        const llmProvider = document.getElementById('llmProvider').value;
         const startBtn = document.getElementById('startBtn');
 
         if (!profile) {
@@ -115,7 +125,8 @@
                 body: JSON.stringify({
                     session_id: sessionId,
                     profile: profile,
-                    job_number: jobNumber || null
+                    job_number: jobNumber || null,
+                    llm_provider: llmProvider
                 })
             });
 
diff --git a/requirements.txt b/requirements.txt
index be4dfe5..afedc65 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -21,6 +21,7 @@ beautifulsoup4==4.12.2
 # LLM Providers
 openai>=1.12.0
 anthropic>=0.18.0
+google-generativeai>=0.5.0
 
 # Video Processing (Video QC + Video Master)
 opencv-python>=4.8.0