Egregore

Egregore: An implementation of LangChain and Claude for Accelerated Competitive Analysis Our Knowledge Harvesting team aimed to create an AI solution for a crucial stage of the product development process: Analyzing competition. Understanding the competition is vital to decipher a market and assess market attractiveness. Accurate knowledge of the competition helps product and marketing managers communicate product functions, features, and benefits effectively. We explored techniques to structure information for LangChain and opted to use prompts, testing them in GPT-4 and Claude 2. Additionally, we experimented with semantic triples to drive agent behaviors. Triples and knowledge graphs are powerful and interpretable ways to structure and curate data. In a real-world application, we focused on five ultrasound devices, enabling rich comparisons, including regulatory requirements. We delved into FDA processes for medical devices. Though we didn't fully structure this complex data, we captured the necessary elements to make it AI-digestible. With a 55-page Integrated Product Management Process as reference, we converted its step-by-step guidance into prompts and agents. While we encountered some uncompleted tests to assess AI configurations, we successfully developed workflows, data structures, and prompts to leverage LangChain's potential. Although more work lies ahead, we established a strong foundation for further exploration. Our team's experience in using AI for a real-world scenario was good! In just 3 days, we covered significant ground, demonstrating the tremendous potential of AI to augment human intelligence. Our project's ambition affirmed the possibilities of utilizing AI to enhance product management and innovation.