diff --git a/site-sleuth/SEO-rival-insights/lambda_function.py b/site-sleuth/SEO-rival-insights/lambda_function.py
new file mode 100644
index 0000000..4cf612a
--- /dev/null
+++ b/site-sleuth/SEO-rival-insights/lambda_function.py
@@ -0,0 +1,238 @@
+import json
+import boto3
+import os
+import requests
+import emoji
+import markdown
+from bs4 import BeautifulSoup, Comment
+
+s3_client = boto3.client('s3')
+lambda_client = boto3.client('lambda')
+
+HEADERS = {
+    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:98.0) Gecko/20100101 Firefox/98.0",
+    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
+    "Accept-Language": "en-US,en;q=0.5",
+    "Accept-Encoding": "gzip, deflate",
+    "Connection": "keep-alive",
+    "Upgrade-Insecure-Requests": "1",
+    "Sec-Fetch-Dest": "document",
+    "Sec-Fetch-Mode": "navigate",
+    "Sec-Fetch-Site": "none",
+    "Sec-Fetch-User": "?1",
+    "Cache-Control": "max-age=0",
+}
+
+def handler(event, context):
+    try:
+        # Extract environment variables
+        bucket_name = os.environ['PI_EXECUTION_S3_BUCKET_NAME']
+        result_folder = os.environ['PI_RESULTS_FOLDER']
+        openai_function = os.environ['PI_OPENAI_FUNCTION']
+        
+        # Extract event data
+        execution_id = event['execution_id']
+        user_id = event['user_id']
+        product_id = event['product_id']
+        token = event['token']
+        custom_inputs = event['custom_inputs']
+        competitor_url = custom_inputs['competitor_url']
+        user_website_url = custom_inputs['user_website_url']
+        
+        # Crawl and extract website content for both the user and competitor websites
+        user_data = crawl_website(user_website_url)
+        competitor_data = crawl_website(competitor_url)
+        
+        # Generate a detailed prompt for OpenAI to analyze the competitor
+        prompt = generate_competitor_analysis_prompt(user_data, competitor_data, user_website_url, competitor_url)
+        
+        # Invoke the OpenAI function for competitor SEO analysis
+        openai_payload = {
+            "execution_id": execution_id,
+            "user_id": user_id,
+            "product_id": product_id,
+            "service": "chat-gpt-4o-mini",
+            "size": "11x",
+            "prompt": prompt
+        }
+        
+        response = lambda_client.invoke(
+            FunctionName=openai_function,
+            InvocationType='RequestResponse',
+            Payload=json.dumps(openai_payload)
+        )
+        
+        response_payload = json.load(response['Payload'])
+        status_code = response_payload.get('status_code')
+        if status_code != 200:
+            raise Exception(f"OpenAI chat function returned {status_code} as status code with body {str(response_payload.get('body'))}")
+        function_result = response_payload.get('body')
+        
+        if function_result is None:
+            raise Exception("No result from OpenAI chat function")
+        
+        # Save the result to S3
+        result_key = f"{result_folder}/{execution_id}/competitor_seo_result.json"
+        s3_client.put_object(Bucket=bucket_name, Key=result_key, Body=json.dumps(function_result, indent=4))
+        
+        # Send the result as HTML to the endpoint
+        html_message = generate_html_message(execution_id, user_id, product_id, function_result)
+        
+        send_result_to_wordpress({
+            "execution_id": execution_id,
+            "user_id": user_id,
+            "product_id": product_id,
+            "token": token,
+            "status": "successful",
+            "results": html_message
+        })
+        
+        return {
+            'statusCode': 200,
+            'body': json.dumps({
+                'message': 'Task executed successfully'
+            })
+        }
+    
+    except Exception as e:
+        print(f"Error: {str(e)}")
+
+        send_result_to_wordpress({
+            "execution_id": execution_id,
+            "user_id": user_id,
+            "product_id": product_id,
+            "token": token,
+            "status": "failed",
+            "results": f"""
+                <div style="padding: 20px; color: #ff3333; background-color: #fec4c4; border-radius: 5px;">
+                    <p><strong>Error: </strong> {str(e)}</p>
+                </div>
+            """
+        })
+
+        return {
+            'statusCode': 500,
+            'body': json.dumps({
+                'message': str(e)
+            })
+        }
+
+def crawl_website(url):
+    response = requests.get(url, headers=HEADERS)
+    html = response.text
+
+    # Parse HTML using BeautifulSoup
+    soup = BeautifulSoup(html, 'html.parser')
+
+    # Extract relevant SEO elements
+    title = soup.title.string if soup.title else "No title found"
+    description = soup.find("meta", {"name": "description"})['content'] if soup.find("meta", {"name": "description"}) else "No description found"
+    keywords = soup.find("meta", {"name": "keywords"})['content'] if soup.find("meta", {"name": "keywords"}) else "No keywords found"
+    h1_tags = [h1.get_text() for h1 in soup.find_all('h1')]
+    h2_tags = [h2.get_text() for h2 in soup.find_all('h2')]
+    h3_tags = [h3.get_text() for h3 in soup.find_all('h3')]
+    alt_texts = [img['alt'] for img in soup.find_all('img') if img.has_attr('alt')]
+    links = [a['href'] for a in soup.find_all('a') if a.has_attr('href')]
+    
+    # Removing unnecessary parts of the HTML
+    # Remove script, style, and comments
+    for script in soup(["script", "style"]):
+        script.extract()
+    comments = soup.findAll(text=lambda text: isinstance(text, Comment))
+    [comment.extract() for comment in comments]
+
+    # Remove unnecessary attributes from tags
+    for tag in soup.find_all(True):
+        # Only keep certain attributes for <a> tags
+        if tag.name == 'a':
+            attrs = {key: tag.attrs[key] for key in ['href'] if key in tag.attrs}
+            tag.attrs = attrs
+        # Remove all attributes for other tags
+        else:
+            tag.attrs = {}
+
+    # Cleaned HTML content
+    cleaned_html = soup.prettify()
+
+    crawled_data = {
+        "title": title,
+        "description": description,
+        "keywords": keywords,
+        "h1_tags": h1_tags,
+        "h2_tags": h2_tags,
+        "h3_tags": h3_tags,
+        "alt_texts": alt_texts,
+        "links": links,
+        "cleaned_html": cleaned_html  # Reduced HTML content for analysis
+    }
+
+    return crawled_data
+
+def generate_competitor_analysis_prompt(user_data, competitor_data, user_website_url, competitor_url):
+    return f"""
+    You are an expert in SEO analysis. I have provided you with the relevant crawled data from two websites: the user's website ({user_website_url}) and a competitor's website ({competitor_url}). 
+    Here is the information for the user's website:
+    
+    - Title: {user_data['title']}
+    - Description: {user_data['description']}
+    - Keywords: {user_data['keywords']}
+    - H1 Tags: {', '.join(user_data['h1_tags'])}
+    - H2 Tags: {', '.join(user_data['h2_tags'])}
+    - H3 Tags: {', '.join(user_data['h3_tags'])}
+    - Alt Texts: {', '.join(user_data['alt_texts'])}
+    - Links: {', '.join(user_data['links'])}
+    - Cleaned HTML: ``` {user_data['cleaned_html']} ```
+    
+    And here is the information for the competitor's website:
+    
+    - Title: {competitor_data['title']}
+    - Description: {competitor_data['description']}
+    - Keywords: {competitor_data['keywords']}
+    - H1 Tags: {', '.join(competitor_data['h1_tags'])}
+    - H2 Tags: {', '.join(competitor_data['h2_tags'])}
+    - H3 Tags: {', '.join(competitor_data['h3_tags'])}
+    - Alt Texts: {', '.join(competitor_data['alt_texts'])}
+    - Links: {', '.join(competitor_data['links'])}
+    - Cleaned HTML: ``` {competitor_data['cleaned_html']} ```
+    
+    Please perform a detailed SEO competitor analysis, focusing on the following aspects:
+    
+    1. Comparative SEO performance of the user's website and the competitor's website.
+    2. Strengths and weaknesses of both websites in terms of SEO.
+    3. Opportunities for the user to improve and outperform the competitor in SEO.
+    4. Specific recommendations for improving the user's website SEO to gain an edge over the competitor.
+    
+    Provide your analysis in a structured format with actionable insights.
+    """
+
+def generate_html_message(execution_id, user_id, product_id, response):
+    response_text = response['choices'][0]['message']['content']
+    emojized_text = emoji.emojize(response_text)
+    html_str = markdown.markdown(emojized_text)
+    return f"""
+        <div style="padding: 20px; background-color: #f0f0f0; border-radius: 5px;">
+            <h2>SEO Competitor Analysis Result</h2>
+            <p><strong>Execution ID:</strong> {execution_id}</p>
+            <p><strong>User ID:</strong> {user_id}</p>
+            <p><strong>Product ID:</strong> {product_id}</p>
+            <p><strong>Analysis Results:</strong><br>
+                <div>{html_str}</div>
+            </p>
+        </div>
+    """
+
+def send_result_to_wordpress(result):
+    post_data = json.dumps(result)
+    wordpress_url = 'https://promptintellect.com/wp-json/product-extension/v1/lambda-results'
+    
+    headers = {
+        'Content-Type': 'application/json',
+        'Content-Length': str(len(post_data))
+    }
+
+    response = requests.post(wordpress_url, data=post_data, headers=headers)
+    
+    if response.status_code != 200:
+        raise Exception(f"Failed to send result to WordPress. Status code: {response.status_code}, Response: {response.text}")
+    
+    return response.text
diff --git a/site-sleuth/URL-SEO-analyzer/lambda_function.py b/site-sleuth/URL-SEO-analyzer/lambda_function.py
new file mode 100644
index 0000000..ee1ed95
--- /dev/null
+++ b/site-sleuth/URL-SEO-analyzer/lambda_function.py
@@ -0,0 +1,229 @@
+import json
+import boto3
+import os
+import requests
+import emoji
+import markdown
+from bs4 import BeautifulSoup, Comment  # Import Comment
+from urllib.request import urlopen
+
+s3_client = boto3.client('s3')
+lambda_client = boto3.client('lambda')
+
+HEADERS = {
+    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:98.0) Gecko/20100101 Firefox/98.0",
+    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
+    "Accept-Language": "en-US,en;q=0.5",
+    "Accept-Encoding": "gzip, deflate",
+    "Connection": "keep-alive",
+    "Upgrade-Insecure-Requests": "1",
+    "Sec-Fetch-Dest": "document",
+    "Sec-Fetch-Mode": "navigate",
+    "Sec-Fetch-Site": "none",
+    "Sec-Fetch-User": "?1",
+    "Cache-Control": "max-age=0",
+}
+
+def handler(event, context):
+    try:
+        # Extract environment variables
+        bucket_name = os.environ['PI_EXECUTION_S3_BUCKET_NAME']
+        result_folder = os.environ['PI_RESULTS_FOLDER']
+        openai_function = os.environ['PI_OPENAI_FUNCTION']
+        
+        # Extract event data
+        execution_id = event['execution_id']
+        user_id = event['user_id']
+        product_id = event['product_id']
+        token = event['token']
+        custom_inputs = event['custom_inputs']
+        website_url = custom_inputs['website_url']
+        
+        # Crawl and extract website content
+        crawled_data = crawl_website(website_url)
+        
+        # Generate a detailed prompt for OpenAI
+        prompt = generate_seo_analysis_prompt(crawled_data, website_url)
+        
+        # Invoke the OpenAI function for SEO analysis
+        openai_payload = {
+            "execution_id": execution_id,
+            "user_id": user_id,
+            "product_id": product_id,
+            "service": "chat-gpt-4o-mini",
+            "size": "9x",
+            "prompt": prompt
+        }
+        
+        response = lambda_client.invoke(
+            FunctionName=openai_function,
+            InvocationType='RequestResponse',
+            Payload=json.dumps(openai_payload)
+        )
+        
+        response_payload = json.load(response['Payload'])
+        status_code = response_payload.get('status_code')
+        if status_code != 200:
+            raise Exception(f"OpenAI chat function returns {status_code} as status code with body {str(response_payload.get('body'))}")
+        function_result = response_payload.get('body')
+        
+        if function_result is None:
+            raise Exception("No result from OpenAI chat function")
+        
+        # Save the result to S3
+        result_key = f"{result_folder}/{execution_id}/seo_result.json"
+        s3_client.put_object(Bucket=bucket_name, Key=result_key, Body=json.dumps(function_result, indent=4))
+        
+        # Send the result as HTML to the endpoint
+        html_message = generate_html_message(execution_id, user_id, product_id, function_result)
+        
+        send_result_to_wordpress({
+            "execution_id": execution_id,
+            "user_id": user_id,
+            "product_id": product_id,
+            "token": token,
+            "status": "successful",
+            "results": html_message
+        })
+        
+        return {
+            'statusCode': 200,
+            'body': json.dumps({
+                'message': 'Task executed successfully'
+            })
+        }
+    
+    except Exception as e:
+        print(f"Error: {str(e)}")
+
+        send_result_to_wordpress({
+            "execution_id": execution_id,
+            "user_id": user_id,
+            "product_id": product_id,
+            "token": token,
+            "status": "failed",
+            "results": f"""
+                <div style="padding: 20px; color: #ff3333; background-color: #fec4c4; border-radius: 5px;">
+                    <p><strong>Error: </strong> {str(e)}</p>
+                </div>
+            """
+        })
+
+        return {
+            'statusCode': 500,
+            'body': json.dumps({
+                'message': str(e)
+            })
+        }
+
+def crawl_website(url):
+    response = requests.get(url, headers=HEADERS)
+    html = response.text
+
+    # Parse HTML using BeautifulSoup
+    soup = BeautifulSoup(html, 'html.parser')
+
+    # Extract relevant SEO elements
+    title = soup.title.string if soup.title else "No title found"
+    description = soup.find("meta", {"name": "description"})['content'] if soup.find("meta", {"name": "description"}) else "No description found"
+    keywords = soup.find("meta", {"name": "keywords"})['content'] if soup.find("meta", {"name": "keywords"}) else "No keywords found"
+    h1_tags = [h1.get_text() for h1 in soup.find_all('h1')]
+    h2_tags = [h2.get_text() for h2 in soup.find_all('h2')]
+    h3_tags = [h3.get_text() for h3 in soup.find_all('h3')]
+    alt_texts = [img['alt'] for img in soup.find_all('img') if img.has_attr('alt')]
+    links = [a['href'] for a in soup.find_all('a') if a.has_attr('href')]
+    
+    # Removing unnecessary parts of the HTML
+    # Remove script, style, and comments
+    for script in soup(["script", "style"]):
+        script.extract()
+    comments = soup.findAll(text=lambda text: isinstance(text, Comment))
+    [comment.extract() for comment in comments]
+
+    # Remove unnecessary attributes from tags
+    for tag in soup.find_all(True):
+        # Only keep certain attributes for <a> tags
+        if tag.name == 'a':
+            attrs = {key: tag.attrs[key] for key in ['href'] if key in tag.attrs}
+            tag.attrs = attrs
+        # Remove all attributes for other tags
+        else:
+            tag.attrs = {}
+
+    # Cleaned HTML content
+    cleaned_html = soup.prettify()
+
+    crawled_data = {
+        "title": title,
+        "description": description,
+        "keywords": keywords,
+        "h1_tags": h1_tags,
+        "h2_tags": h2_tags,
+        "h3_tags": h3_tags,
+        "alt_texts": alt_texts,
+        "links": links,
+        "cleaned_html": cleaned_html  # Reduced HTML content for analysis
+    }
+
+    return crawled_data
+
+def generate_seo_analysis_prompt(crawled_data, website_url):
+    return f"""
+    You are an expert in SEO analysis. I have provided you with the relevant crawled data from the website: {website_url}. 
+    Here is the information:
+    
+    - Title: {crawled_data['title']}
+    - Description: {crawled_data['description']}
+    - Keywords: {crawled_data['keywords']}
+    - H1 Tags: {', '.join(crawled_data['h1_tags'])}
+    - H2 Tags: {', '.join(crawled_data['h2_tags'])}
+    - H3 Tags: {', '.join(crawled_data['h3_tags'])}
+    - Alt Texts: {', '.join(crawled_data['alt_texts'])}
+    - Links: {', '.join(crawled_data['links'])}
+    
+    Here is the cleaned HTML content of the webpage (unnecessary tags and scripts removed):
+    {crawled_data['cleaned_html']}
+    
+    Please analyze this website in terms of SEO and provide detailed insights and recommendations. Your analysis should include, but not be limited to, the following:
+    
+    1. Overall SEO performance of the website.
+    2. Issues with meta tags, titles, and descriptions.
+    3. Use of headings (H1, H2, H3, etc.) and their effectiveness.
+    4. Keyword optimization and suggestions for improvement.
+    5. Use of alt texts for images and suggestions for improvement.
+    6. Analysis of internal and external links.
+    7. Any missing critical SEO elements.
+    8. Recommendations for improving the website's SEO.
+    
+    Provide your analysis in a structured format with actionable insights.
+    """
+
+def generate_html_message(execution_id, user_id, product_id, response):
+    response_text = response['choices'][0]['message']['content']
+    emojized_text = emoji.emojize(response_text)
+    html_str = markdown.markdown(emojized_text)
+    return f"""
+        <div style="padding: 20px; background-color: #f0f0f0; border-radius: 5px;">
+            <h2>SEO Analysis Result</h2>
+            <p><strong>Execution ID:</strong> {execution_id}</p>
+            <p><strong>User ID:</strong> {user_id}</p>
+            <p><strong>Product ID:</strong> {product_id}</p>
+            <p><strong>Analysis Results:</strong><br>
+                <div>{html_str}</div>
+            </p>
+        </div>
+    """
+
+def send_result_to_wordpress(result):
+    post_data = json.dumps(result)
+    wordpress_url = 'https://promptintellect.com/wp-json/product-extension/v1/lambda-results'
+    
+    headers = {
+        'Content-Type': 'application/json',
+        'Content-Length': str(len(post_data))
+    }
+    
+    response = requests.post(wordpress_url, headers=headers, data=post_data)
+    
+    if response.status_code != 200:
+        raise Exception(f"Unexpected status code: {response.status_code}, {response.text}")