AppEvolve, a leading mobile and web development firm, wanted to streamline their engineers’ access to the latest best practices and standards. Engineers often spent valuable time navigating Notion to find necessary details, detracting from their development work. Tech 42 implemented a cutting-edge solution leveraging AWS Knowledge Bases, AWS Bedrock, and Pinecone. This AI-driven chatbot was seamlessly integrated into Slack, enabling engineers to interact naturally with company data stored in S3, fetched directly from Notion.

The challenge

AppEvolve’s engineering team encountered hurdles in efficiently accessing up-to-date company best practices and engineering documentation. The manual process of combing through Notion for specific information proved to be a drain on time, affecting productivity and innovation. The team’s frustration underscored the need for an intuitive, real-time solution that could maintain AppEvolve’s competitive edge and streamline workflows. Moreover, engineers expressed a preference for a more natural and conversational way to query company best practices. Recognizing these needs, a transformative approach was necessary to facilitate easy access to critical information and support the team.

The solution

To address this challenge, Tech 42harnessed the power of AI and AWS to create a novel solution that bridges the gap between company data and accessibility. By integrating AWS Knowledge Bases and AWS Bedrock, we crafted an AI chatbot tailored for Slack. This chatbot enables real-time interaction with data stored in S3, automatically backed up from Notion nightly. Engineers can now query the chatbot in natural language within their Slack environment to retrieve and interact with the latest company practices and documentation.

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Implementation

‍The implementation involved several key steps:

• Data Backup: We set up an automated process to back up all engineering documentation and best practices from Notion to S3 nightly, ensuring the chatbot interacts with the most current data.
• AI Integration: Using AWS Bedrock’s LLM capabilities and AWS Knowledge Bases, we developed a chatbot capable of understanding natural language queries and fetching relevant information from the data stored in S3. Embeddings are automatically created from source data and stored in Pinecone.
• Slack Integration: The chatbot was integrated into AppEvolve’s Slack workspace, allowing engineers to interact with it directly from their primary communication tool.

The outcome‍

The introduction of this AI-driven solution into AppEvolve’s operations has led to improvements in efficiency and productivity. Engineers can now access the most current best practices and documentation in seconds, directly within Slack. Now instead of searching Notion, engineers simply ask a question to the bot in Slack. The bot will search Notion documentation and provide a relevant answer. This has not only streamlined day-to-day operations, but also enhanced the quality of engineering and development time.

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Benefits

• Increased Efficiency: Engineers save time by accessing information quickly and conversationally.
• Improved Accuracy: Automatic nightly updates ensure the data is current, reducing the risk of outdated practices.
• Enhanced Collaboration: Easy access to shared knowledge fosters a collaborative environment.
• Scalability: The solution easily scales with the growth of the company and its data.

Conclusion

This AI-driven chatbot solution for AppEvolve represents a leap forward in how teams access and interact with company data. By leveraging AWS’s powerful cloud and AI technologies, combined with Slack, we’ve created a seamless, intuitive way for engineers to stay informed and aligned with company standards. This case study showcases our commitment to pioneering innovative solutions that address real-world challenges, and driving business success.