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Unlocking the Future of AI-Driven Workflow Management with an Open Semantic Layer

· 3 min read
Dries Van Hansewijck
Dries Van Hansewijck
AI Architect

Abstract​

In the world of modern information management and AI-integrated systems, we strive for innovation that goes beyond conventional solutions. This whitepaper introduces a revolutionary concept: an open-source semantic layer within a document and conversation management system (DMS + CIM), inspired by the principles of MemGPT. Through a collaborative community approach, we aim to help companies overcome inefficiencies and extract unprecedented value from their data and interactions.

1. Context and Challenges​

In a digital world where data and conversations grow exponentially, companies face challenges such as:

  • Information flow silos: Legacy systems like SharePoint limit collaboration
  • Limited context in AI-based solutions: Most LLMs can only process limited amounts of context
  • Lack of accessibility: Current systems often require expertise and infrastructure that aren't scalable or flexible

2. Our Innovative Approach​

We introduce an open semantic layer with the following core components:

  1. Conversational Intelligence Mining (CIM):

    • Capture conversations, convert them into searchable transcripts, and generate insights like action items and trends
    • Analyze sentiments and detect recurring themes

  2. Prompt Database:

    • Provides companies with standardized and optimized prompts for consistent AI interactions
    • Supports user training and onboarding

  3. AccelerIQ Method:

    • An acceleration model for implementing AI solutions within weekly schedules, focusing on iteration and user-friendliness

  4. Semantic Open Layer:

    • An open-source framework that communicates between internal systems, documents, and user interactions
    • Promotes collaboration and innovation within a community of developers and users

3. MemGPT as Inspiration​

Our approach is inspired by MemGPT's use of multi-level memory management:

  • Virtual Context Management: LLMs use limited context effectively through hierarchical memory management
  • Persistent Memory Integration: Information is stored outside the LLM's context and brought back on-demand
  • Open Architecture: Like MemGPT, our semantic layer focuses on scalable and modular interaction between AI and datasets

4. Semantic Layer Functionalities​

  1. Interoperability:

    • Seamlessly integrate with existing systems like SharePoint or Google Workspace

  2. Automatic Data Analysis:

    • Connect structured and unstructured data in real-time

  3. Accessible APIs:

    • Allow developers to build on a shared framework and create new applications

  4. User-Friendly Interaction:

    • Provide end users with visual dashboards and intuitive workflows

5. Open Source and Community-Driven Innovation​

We're opening the semantic layer as an open-source project to:

  • Stimulate collaboration: Developers and companies can contribute to the framework's evolution
  • Promote transparency: A shared ecosystem that builds user trust
  • Ensure scalability: Harness the power of collective innovation for global impact

6. Use Cases and Impact​

  1. Healthcare:

    • Use CIM to analyze patient conversations and develop improved care pathways

  2. Finance:

    • Analyze documents and conversations to manage compliance and risks

  3. Education:

    • Develop personalized learning paths based on student data and interactions

7. Community Call-to-Action​

We invite developers, companies, and innovators to participate in the platform's development. By working together on the semantic layer, we can push the boundaries of AI-integrated information management.

8. Conclusion​

With an open-source approach and the power of a semantic layer, we focus on transforming how organizations handle data and workflows. Together with the community, we're creating a system that not only solves today's problems but embraces tomorrow's challenges.