Artificial Mind Core Architecture Design

Building the cognitive foundation for truly autonomous AI agents

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🎯 TL;DR - Key Takeaways

  • • 60% reduction in human oversight requirements vs traditional AI
  • • Autonomous task management without performance degradation
  • • Foundation architecture for all Virtual Employee applications
  • • Successfully deployed in AI-Voice with 95% user satisfaction

🏢 Business Context

The Problem

Traditional AI systems require constant human supervision and cannot work proactively like human employees. Businesses need AI that thinks, decides, and acts autonomously.

Our Competitive Advantage

Our cognitive architecture combines decision-making, memory, and learning systems in a way that enables true autonomous behavior - not just automated responses.

⚙️ Our Approach

🛡️ Proprietary Technology - Core implementation details remain confidential pending patent applications

Proprietary cognitive architecture combining hierarchical decision-making systems, episodic memory layers, and goal-driven attention mechanisms. The system learns and evolves while maintaining consistent performance.

Technology Stack

Cognitive Architecture Memory Systems Decision Engines Learning Algorithms

📊 Results & Metrics

Before
Requires 80% human oversight
→
After
Requires 20% human oversight
60% reduction in human intervention

Key Findings

Key insight: Hierarchical decision-making with episodic memory enables human-like autonomous behavior. The system learns context and adapts decisions accordingly.

💼 Business Impact

Product Impact

Foundation for all Aisberg Virtual Employees - AI-Voice, AI-Scraper, and future products all built on this architecture

Timeline to Market

Technology deployed in AI-Voice (February 2024), powering all current and future products

Next Steps

🏷️ Experiment Details

Experiment ID

AMCA-2024-001

Duration

February 1, 2024 → February 28, 2024

Status

completed

Research Team

  • • David Iftime (Principal Researcher)
  • • Vladimir Iacob (Technical Lead)

Validation Method

6-month production testing with real customer workloads and continuous performance monitoring

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