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Federated AI

Explore Blankstate's approach to collaborative AI, enabling powerful insights while preserving data privacy and context across distributed systems.

The Core Engine: Intention Blended Framework (IBF)

Blankstate's Federated AI capabilities are powered by our proprietary core technology: the Intention Blended Framework (IBF). This advanced system is the engine that drives sophisticated understanding and analysis within both real-time and retrospective contexts.

IBF moves beyond simple data processing to interpret the nuances of interaction—context, intent, sequence, and underlying dynamics. It's the key to unlocking meaningful insights from distributed data sources without compromising privacy or centralizing raw information.

Functional Role: Powering Insights Across Solutions

IBF is integral to how Blankstate analyzes interactions and generates value:

  • In Stream (via Phantom):

    Enables sophisticated, real-time evaluation of local interactions against Protocols.

    • North Star Guidance: Contextual cues based on interaction patterns.
    • Catalyst Detection: Identifying success sequences (planned V2+).
  • In Replay:

    Serves as the core analysis engine for deep dives into document content.

    • Accurately measures against Metamarkers.
    • Identifies relevant Markers.
    • Understands entity relationships.
    • Generates meaningful rationales.
    • Reconstructs the "State of Entities".

Technical Foundation: Advanced AI/ML

IBF leverages an advanced AI/ML framework combining elements from:

  • Self-supervised language understanding
  • Sequence modeling
  • Cognitive analysis principles

Distinct implementations cater to different needs:

  • Lightweight (Phantom): Optimized for efficient, real-time, local processing.
  • Computationally Intensive (Replay): Designed for deep, retrospective analysis.
Proprietary Technology: The specific algorithms and architecture of IBF are Blankstate intellectual property and are not publicly disclosed.

Foundation in Research

The conceptual underpinnings guiding the development of IBF are discussed in the following research:

Self-supervised Meta-Heuristic Mapping - a Framework for Adaptive Human-AI Governance

Mehdi Cheraitia (blankstate.ai)

"...maps behavioral and cultural 'blueprints' to bridge personal and institutional realms... enabling co-evolving human-AI understanding by integrating individual empowerment and organizational guidance through privacy-first dynamic meta-modeling..."

Access the full paper: DOI: 10.13140/RG.2.2.30201.48486

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