AsterMind provides a neuro-symbolic intelligence platform called EVO that learns continuously from real-time data streams, creating digital clones of observed systems. The platform trains in seconds without GPUs, runs on low-footprint infrastructure, and works in air-gapped environments, reducing LLM token costs to zero. It also offers ELM-Community Edition, a free open-source JavaScript Extreme Learning Machine library with 21+ variants.
Funding
Funding not disclosed
Founders
Product
Problem
Most AI systems based on LLMs are structurally unsuited for mission-critical real-time data streams, requiring expensive centralized infrastructure and external services that limit deployment options. Traditional neural networks also demand iterative backpropagation with GPUs, making them costly, slow to train, and difficult to trust in regulated environments where evidence and traceability are essential.
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