Artificial Mind Core Architecture Design
Ongoing research developing cognitive frameworks for autonomous AI behavior
Overview
- 1 60% reduction in human oversight requirements vs traditional AI
- 2 Autonomous task management without performance degradation
- 3 Foundation architecture for all Virtual Employee applications
- 4 Successfully deployed in AI-Voice with 95% user satisfaction
The Problem We're Solving
Current Challenge
Traditional AI systems require constant human supervision and cannot work proactively like human employees. Businesses need AI that thinks, decides, and acts autonomously.
Our Advantage
Our cognitive architecture combines decision-making, memory, and learning systems in a way that enables true autonomous behavior - not just automated responses.
Our Methodology
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
Results & Performance
Key Findings
Key insight: Hierarchical decision-making with episodic memory enables human-like autonomous behavior. The system learns context and adapts decisions accordingly.
Market 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
Team & Validation
What's Next
Next Steps
6-month production testing with real customer workloads and continuous performance monitoring
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