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AsterMind is an AI Platform that includes Cybernetic AI components that trains instantly, is self-learning and adapts automatically to changes in its environment. It is always up to date, runs in the cloud or locally without internet access and can improve the use of current AI solutions dramatically.
It is not a static AI model but a self-adapting and self-learning AI Solution with Cybernetic elements. It can solve AI problems that are not possible with LLMs. Use AsterMind in RAG Solutions, Data Pipelines, Live data streams and more.
A new class of AI systems that combine machine learning with cybernetic control principles
Trains in milliseconds, not hours or days. No GPU infrastructure required.
Continuously learns and adapts automatically to changes in its environment.
Maintains persistent state and context, staying current without retraining cycles.
Runs in the cloud or locally without internet access with full functionality.
Reduces LLM API calls by 4-5x while improving accuracy and consistency.
Feedback loops, regulation, and adaptation under constraint for stable operation.
Built on established foundations in cybernetics, online learning, and control systems—not speculative AI claims
Designed for deployment across the full enterprise stack—from data infrastructure to edge systems
Retrieval Augmented Generation with persistent context and selective model invocation
Schema-aware data transformation with automatic drift adaptation
Real-time processing with adaptive handling of non-stationary inputs
Local intelligence with operation under connectivity constraints
AsterMind Advanced AI is not intended to replace LLMs but to serve as a complementary intelligence and control layer
Static model requiring retraining
Self-adapting with continuous online learning
Stateless inference calls
Persistent state across time
Brute-force model invocation
Selective, cost-aware AI coordination
Fragile under schema drift
Automatic adaptation to change
Cloud-dependent operation
Cloud or local with full autonomy
Unpredictable costs at scale
Bounded, predictable operation
Reduction in LLM API Calls
Achieve higher benchmark scores with significantly fewer model invocations
Annual Cost Savings
Stabilizing pipelines and reducing rework saves tens of millions in operational costs
Autonomous Operation
Reduced need for constant human supervision with bounded, auditable control
Common questions about AsterMind AI Cybernetic Platform