Mimir AI: The Intelligent Enterprise Assistant

This project is titled Intelligent Enterprise Assistant: Enhancing Organizational Efficiency through AI-driven Chatbot Integration

Key Highlights:

1. Problem Statement:

   • Developing an AI-powered chatbot to enhance organizational efficiency using advanced NLP, document recognition, and real-time responses.

2. Proposed Solution:

   • Features a Digital Human Avatar for lifelike, context-aware interactions.
   • Provides up-to-date, summarized information for better decision-making.
   • Supports multiple input types: text, voice, image, and documents.
   • Offers keyword extraction, query resolution in less than 5 seconds, and multi-user concurrency (10+ users).
   • Includes security measures like email-based two-factor authentication and inappropriate language blocking.

3. Technical Approach:

   • Input Handling: Users can input text, audio (speech-to-text), or upload documents.
   • Data Processing: Uploaded documents are stored on Amazon S3 via presigned URLs, chunked, and embedded for efficient retrieval.
   • Response Generation: Uses the Gemini LLM to process requests and generates text or speech outputs.
   • Digital Avatar Integration: The digital human provides responses with real-time lip-sync and multilingual support.

4. Challenges and Risks:

   • Real-time response and lip-sync latency for a smooth user experience.
   • Multilingual lip-syncing requires manual adjustments.
   • Integration of Unreal Engine with LLMs demands custom C++ handlers.

5. Impact and Benefits:

   • Multilingual Support: Accessible to a broader user base.
   • Accessibility Features: Supports users with disabilities.
   • Cost Reduction: Automates operations to reduce expenses.
   • Environmental Benefits: Cloud-based architecture minimizes the physical infrastructure and carbon footprint.

6. Feasibility and Revenue Potential:

   • Potential revenue streams include subscriptions, licensing, and enterprise solutions.
   • Scalable chatbot services ensure cost efficiency with automation.

7. Current Progress:

   • The project is 60% complete, with the next steps involving testing and validation.

8. Future Enhancements:

   • Optimization of lip-sync technology and context maintenance through machine learning for automatic file tagging and context recovery.

Built With

• c++
• langchain
• python