Guiding Principles
Leveraging Generative AI to capture and transform data by interacting with Subject Matter Experts and tribal knowledge.
Knowledge Capture
Facilitates the capture of SME and tribal knowledge by interacting with Generative AI, ensuring valuable insights are integrated into the data transformation process.
AI Driven Data Transformation
Uses advanced Generative AI models to automate data transformation, ensuring alignment with domain-specific requirements while retaining expert knowledge.
Collaboration-Centric
Emphasizes collaboration between the AI and SMEs, where AI models serve as tools to enhance human expertise, rather than replace it.
Accuracy & Consistency
Ensures high data accuracy and consistency by validating and refining the transformation process based on expert feedback and predefined rules.
Scalability
Supports scalable data processing, adapting to varying volumes of data and continuously improving as more SME knowledge is integrated.
Flexibility
Capable of processing diverse datasets and adapting to specific business contexts by incorporating specialized domain knowledge through AI interaction.
Security & Privacy
Implements robust data protection mechanisms to ensure that sensitive knowledge and data are securely handled throughout the process.
Features
- Generative AI Interaction: Engages SMEs through the AI to incorporate their domain expertise directly into the data transformation workflow, enhancing AI-driven decision-making.
- Tribal Knowledge Integration: Captures tacit, informal knowledge and expertise that might otherwise be lost, using Generative AI to distill and formalize this knowledge into usable data formats.
- Automated Data Transformation: Uses AI models to convert raw input data into structured, validated formats based on domain-specific transformations informed by SME interaction.
- Real-Time Expert Feedback: Allows SMEs to provide real-time feedback to refine and validate AI models, improving the quality and accuracy of the transformations over time.
- Contextual Data Processing: Leverages both AI and expert insights to contextualize data, ensuring transformations align with specific business objectives and requirements.
- Continuous Learning: The AI system continuously learns from new interactions with SMEs, evolving its models to reflect the latest expert insights and improving over time.
- Schema-Driven Output: Transformed data is stored in a structured, consistent format aligned with predefined schemas, making it easy to retrieve and use in subsequent business processes.
- Collaborative Review Process: Provides mechanisms for SMEs to review and approve transformed data, ensuring that data meets business and operational standards before being finalized.