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Self-Aware Cerebrum

Autonomous AI Framework for Self-Evolution
AI
Self-Improving Systems
Autonomous Agents
Cognitive Architecture
LLM
AGI Foundation

Founded: October 2024

Stage: Early Seed

Company Overview

Self-Aware Cerebrum is developing a revolutionary AI framework that enables artificial intelligence systems to understand, modify, and extend their own capabilities. Our technology represents a significant advancement in AI autonomy, providing systems that can perform deep technological research, develop complex solutions, and evolve over time with minimal human intervention.

The Cerebrum framework introduces a hierarchical task architecture with integrated advanced capabilities like adaptive context management, parameter optimization, and self-modification within appropriate safety boundaries. This allows AI systems to maintain continuous operation across extended timeframes while providing complete visibility and control to human supervisors.

Our technology represents a foundational step toward more capable artificial general intelligence (AGI) systems that can adapt to new challenges without requiring constant retraining or redevelopment, while maintaining a robust safety framework through multi-level human supervision.

Technology & Architecture

The Self-Aware Cerebrum is built on a multi-layer architecture that enables unprecedented autonomy while maintaining safety and oversight:

  • Core Intelligence Layer

    Manages context, parameters, and knowledge with advanced compression techniques and semantic memory systems, ensuring coherent understanding across operations.

  • Planning Engine

    Autonomously analyzes goals, breaks them down into actionable tasks, and constructs comprehensive plans with dynamic resource allocation.

  • Execution Engine

    Intelligently selects and executes tasks based on context, with capabilities for parallel processing and adaptive task prioritization.

  • Self-Modification Engine

    Enables the system to safely modify its own code through a controlled process with multiple validation checks and human approval gates.

  • Observability Layer

    Provides comprehensive visibility into the system's operation with configurable reporting and intervention capabilities for human supervisors.

Key Technical Innovations:

Our patented Cognitive Loop architecture enables continuous operation through understanding, planning, executing, reflecting, and modifying phases, with seamless transitions between them. This is combined with our Multi-Human Supervision model that provides differentiated oversight roles without disrupting system operation.

Market Opportunity

The global artificial intelligence market size was valued at $136.6 billion in 2022 and is projected to reach $1.8 trillion by 2030, growing at a CAGR of 38.1%. The emerging field of autonomous AI systems represents a significant new segment within this expanding market.

Key Market Drivers:
  • Growing demand for AI systems that can adapt without constant retraining
  • Increasing complexity of technological challenges requiring sustained research
  • Rising costs of AI development and need for systems that can self-improve
  • Emergence of general-purpose AI assistants that require more autonomy
  • Enterprise need for AI systems that can operate continuously across extended timeframes
Target Markets:

Initially focusing on research organizations, technology companies developing AI infrastructure, and specialized fields requiring continuous AI operation like pharmaceutical research, materials science, and complex systems design. The technology has potential applications across any domain requiring adaptive, long-running AI capabilities.

Team

Leadership:
  • Sagiv Levi, CEO
    Experienced technology leader with expertise in AI systems and business development.
  • Shauli Rajuan, Head of Business Development & Chairman
    Holds Masters in Business. Strategic business development expert with extensive experience in technology partnerships and growth.
Team Size:

7 full-time employees: 2 executives, 4 senior AI researchers/engineers, 1 operations

Milestones & Roadmap

Achieved:
  • Core architecture design and proof-of-concept (Oct 2024)
  • Alpha implementation of Self-Aware Cerebrum framework
  • Research partnership with Stanford AI Lab
Next 12-18 Months:
  • Q2 2025: Complete beta implementation with all five layers
  • Q3 2025: First enterprise pilot with research organization
  • Q1 2026: Launch commercial version 1.0
  • Q2 2026: Series A funding
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