AsterMind AI
AsterMind AI provides a cyber security platform that detects attacks by analyzing behavior and evidence rather than relying on signatures or generative AI. The platform's "Detective" component builds case files with evidence, ruled-out explanations, and verdicts, running entirely on customer hardware without LLMs or GPUs. It includes a 3D network visualization tool called Overwatch and an audit chain that cryptographically seals all case actions.
- Artificial Intelligence
- Cybersecurity
- Developer Tools
- AI Agents
- Software Only
- Data & Analytics
Funding
Founders
Founder details are not available yet.
Product
Problem
Conventional AI systems based on LLMs and transformers are structurally unsuited for mission-critical, real-time data streams, requiring expensive centralized infrastructure, GPU-based training, and external services that limit deployment options and introduce trust concerns in regulated environments where evidence and traceability are essential. Traditional neural networks also demand iterative backpropagation with GPUs, making them costly, slow to train, and difficult to audit.
Solution
AsterMind delivers the EVO Platform, a neuro-symbolic intelligence system that learns from live environments and adapts as conditions change, creating digital clones of real-world systems. EVO trains instantly in seconds without GPU requirements, continuously learns from live data streams, and runs on up to 90% less CPU and memory compared to conventional AI. The platform provides validated results with reproducible evidence, supports cloud, on-premise, and air-gapped deployment, and includes simulation and scenario evaluation capabilities. Additionally, AsterMind offers ELM-Community Edition, a free open-source JavaScript Extreme Learning Machine library that enables real-time training and inference directly in browsers or Node.js environments.
Target Audience
Primary customers are enterprise organizations and developers needing mission-critical AI for real-time streaming data workloads, including frontend developers who can integrate machine learning directly into JavaScript or TypeScript applications without server communication.
Features
- Neuro-symbolic AI architecture combining neural networks with symbolic reasoning for explainable decision-making
- Instant Training that completes in seconds without GPU hardware, using closed-form analytical solutions rather than iterative backpropagation
- Continuous learning from live environments with automatic adaptation to data drift and distribution shifts
- Digital clones of observed systems enabling simulation and scenario evaluation before real-world deployment
- Validated results with reproducible evidence for auditability in regulated environments
- Deployment flexibility across cloud, on-premise, and air-gapped environments with significantly lower infrastructure footprint
- EVO Virtual Assistant with neuro-symbolic grounding that operates with or without an LLM (BYOLLM) and reduces LLM calls through neuro-symbolic caching
- ELM-Community Edition includes 4 core ML models (ELM, KernelELM, OnlineELM, DeepELM), 21+ ELM variants, full JavaScript/TypeScript support, and zero external dependencies for browser-based operation