Did you know that organizations spend an average of 19% of their workweek searching for and gathering information? According to research from McKinsey & Company, inefficient knowledge management leads to wasted time and lost productivity—hindering teams from making real progress on meaningful work. (Source, McKinsey)

In today’s digital-first environment, managing and organizing information is critical—whether in education, project management, or team collaboration. Confluence is a widely used knowledge management platform that enables teams to create, share, and manage content effectively. However, ensuring content relevance, completeness, and structure across multiple Confluence pages remains challenging.

By the end of this blog, you’ll learn how to leverage AWS services to automate content verification in Confluence, identify and flag missing or outdated information, and send real-time Slack notifications. This approach enhances knowledge management and boosts productivity, ensuring teams have immediate access to well-structured and up-to-date information.

Real-World Use Cases

The Content Analysis Tool can be applied in multiple industries. Here are some key use cases:

1. Education: Verifying Curriculum Completeness

Educational institutions that use Confluence to store and manage their course materials. However, ensuring that these materials align with academic requirements can be difficult. The tool helps by:

• Identifying missing topics, redundant content, or misaligned materials within course documents.
• Comparing curriculum outlines with existing Confluence pages.
• Sending real-time Slack notifications to instructors when gaps are detected, ensuring materials remain complete and up-to-date.

2. Project Management: Documentation Audits

Project teams need well-documented specifications, user guides, and release notes, but these documents often go missing or outdated. This tool:

• Audits Confluence pages against expected project documentation requirements.
• Flags outdated or incomplete documentation and alerts project managers.
• Ensures teams always have access to the latest information.

3. Team Collaboration: Wiki Maintenance

Team wikis are essential for knowledge sharing but can quickly become disorganized or outdated. This tool:

• Scans wiki pages for missing or irrelevant content.
• Helps teams maintain well-structured internal knowledge bases.
• Ensures mandatory topics are covered and provides alerts for outdated pages.

4. Regulatory Compliance: Policy Document Review

Compliance teams must ensure that policy documents are complete and meet regulatory standards. This tool:

• Verifies if all required policies are documented correctly.
• Identifies gaps or inconsistencies in compliance-related documents.
• Notifies compliance officers about missing or outdated policies to mitigate risks.

5. Practice Exam Analysis for AWS, Azure, and Google Cloud Certifications

Certification exams require comprehensive coverage of topics outlined in official exam guides. This tool can help by:

• Checking practice exams stored in Confluence against the required topics from AWS, Azure, and Google Cloud certification guides.
• Identifying missing exam questions that do not cover essential concepts.
• Sending Slack alerts to instructors or content creators when gaps are detected, ensuring all practice exams align with certification requirements.
• Helping training providers maintain an up-to-date and well-structured practice exam database.

How the Tool Works

This tool automates content analysis through the following key steps:

1. Fetch Data from Confluence

• Uses the Confluence REST API to retrieve page content based on specified topics or categories.
• Ensures that relevant pages are fetched for processing using search queries and filtering mechanisms.

2. Analyze Content

• AWS Lambda functions parse and extract key terms from the retrieved content.
• Topics and keywords are cross-referenced with predefined lists to determine coverage and gaps.
• Text processing techniques, such as tokenization and keyword frequency analysis, are applied to ensure accurate classification.

3. Generate Reports

• Summarizes covered topics, missing elements, or out-of-scope items.
• Provides structured JSON or formatted reports that can be exported for further analysis.
• Generates insights into content quality, structure, and completeness, aiding decision-makers in improving documentation.

4. Notify Teams via Slack

• Sends Slack notifications with detailed insights into content gaps and completeness.
• Includes clickable links to Confluence pages for quick reference and correction.
• Categorizes notifications based on urgency, ensuring high-priority gaps receive immediate attention.

Technical Implementation

Example Demonstration

To illustrate how the Content Analysis Tool works, we can simulate a real-world example. Below, we present a curriculum completeness check for an Introduction to Computer Science course. This example includes screenshots of five Confluence pages and a structured curriculum table.

Curriculum Topics (Criteria for Evaluation)

The following table outlines the key foundational topics that should be covered in the Confluence knowledge base. For this demo, the Curriculum Topics were also uploaded as another confluence page:

Sample Confluence Pages:

1. Introduction to Artificial Intelligence – Covers the fundamentals of Artificial Intelligence, including its history and modern applications.

2. Data Structures and Algorithms – Provides an overview of core data structures but lacks coverage of algorithms.

3. Fundamentals of Cybersecurity – Offers a broad introduction to cybersecurity concepts, covering risk assessment and common security threats.

4. Introduction to Cloud Computing – Explains fundamental cloud computing concepts but does not include practical hands-on tools or industry best practices.

5. Web Development Basics – Covers foundational web technologies, including HTML, CSS, JavaScript, and best practices for modern web development.

Implementation Details

Prerequisites

• AWS Account with appropriate permissions.
• Confluence API credentials to fetch content.
• Slack workspace with webhook access for notifications.

For simplicity, this demo uses AWS Lambda as the core compute component for all operations. The lambda fetches page content from Confluence REST API and analyzes content for topic classification. Then, it will report the result to a Slack channel. Here is the code for this demonstration: 

Code Explanation:

parse_table(html_content)

• Purpose: Extracts data from HTML tables on Confluence pages.
• How: Uses regex to find <table>, <tr>, and <td> tags, then removes HTML tags and whitespace to get clean text.
• Key Output: Returns table data as a list of rows.

fetch_confluence_page(page_url)

• Purpose: Fetches the raw HTML content of a Confluence page.
• How: Sends an HTTP GET request with Basic Authentication (using encoded credentials).
• Key Output: Returns the unescaped HTML content or None if the request fails.

extract_topics_and_keywords(table_data)

• Purpose: Organizes topics and their associated keywords from parsed table data into a dictionary.
• How: Iterates through rows, assigning topics to keywords based on structure.
• Key Output: A dictionary mapping topics to lists of keywords.

search_confluence(search_text, space_key)

• Purpose: Searches Confluence for pages matching a topic using Confluence Query Language (CQL).
• How: Sends a GET request to the Confluence REST API and processes the JSON response.
• Key Output: A list of matching pages (URLs, titles, IDs).

get_document(atlassian_id)

• Purpose: Fetches and parses the body of a Confluence page in atlas_doc_format.
• How: Uses the atlassian_id to retrieve content via an authenticated API request.
• Key Output: A cleaned, lowercase string of the page’s text content.

check_keyword_coverage(search_results, topics_and_keywords)

• Purpose: Checks if the extracted keywords are present in the fetched Confluence pages.

• How: Compares page content to expected keywords for each topic.
• Key Output: A dictionary showing covered and missing keywords for each topic.

send_slack_notification(keyword_coverage)

• Purpose: Sends a formatted Slack message summarizing keyword coverage.
• How: Formats a message with topics, covered/missing keywords, and page links, then posts it to Slack using a webhook.
• Key Output: Sends the notification and logs the status.

lambda_handler(event, context)

• Purpose: Orchestrates the Lambda workflow:
  1. Parses event data for Confluence page details.
  2. Fetches page content and extracts topics/keywords.
  3. Searches Confluence for related pages.
  4. Checks keyword coverage.
  5. Sends a Slack notification.
• Key Output: Returns HTTP status and a success/error message. Logs intermediate outputs to CloudWatch.

Steps

1. Deploy the Lambda Function:

• Ensure your Lambda function is deployed and configured with the necessary environment variables.
• Confirm that your function has proper permissions to access AWS Lambda, Confluence REST API, and the Slack webhook.

2. Create a Test Event in AWS Lambda:

• Go to your Lambda function in the AWS Management Console.
• Click on Test and create a new test event.
• Paste the JSON input provided above.

{
  "page_link": "https://[your-confluence-domain].atlassian.net/wiki/spaces/[your-space-key]/pages/[page-id]/Curriculum+Topics",
  "space_key": "[your-space-key]"
}

• Save and execute the test.

3. Validate CloudWatch Logs:

a. Open the CloudWatch Logs service in AWS.

b. Locate the log group associated with your Lambda function.

c. Review the logs to ensure:

i.  Topics and keywords are extracted successfully.

ii. Page id’s, URLs, and page title was  successfully retrieved.

iii.  The missing keywords and topics are successfully identified.

4. Received the Slack Message Notification:

a. After successful execution, the Slack channel linked to your webhook should receive a notification in this format:

Considerations for Larger Datasets

The demonstration above provides a simplified approach to fetch and analyze content in Confluence. However, if the number of documents or datasets to be processed becomes too large, the architecture can be scaled and optimized for better performance and reliability. A scalable architecture could involve:

1. AWS Step Functions: Orchestrate workflows for managing multiple Confluence page fetches and analyses in parallel.
2. Amazon S3: Store intermediate data or large datasets from Confluence for further processing.
3. Amazon DynamoDB: Maintain a persistent record of processed pages, ensuring efficient retries and status tracking.

When scaling the solution, the following issues might arise:

1. API Rate Limiting – ensure that you implement exponential backoff for retries.
2. Missing Permissions – Verify AWS IAM roles and Confluence API credentials.
3. Webhook Failures – Check Slack API tokens and ensure proper webhook configuration.

This enhanced approach ensures the solution remains robust and performs well under high data loads while allowing for error handling and state management.

Conclusion

Effective knowledge management is essential in today’s digital-first world. By automating content verification, keyword analysis, and real-time notifications, this solution enhances productivity and ensures teams have access to accurate, up-to-date information.

While the approach is simplified, it provides a scalable foundation. The architecture uses AWS Step Functions, S3, and DynamoDB to efficiently handle larger datasets, empowering organizations to focus on meaningful work rather than manual audits. This solution fosters collaboration, decision-making, and innovation by addressing challenges like API limits and permissions.

References:

• Confluence REST API: Page
• Confluence REST API: Search