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We believe the world deserves the AI we always imagined.

EVO is a neuro-symbolic intelligence platform that understands how systems behave.

EVO learns patterns and relationships continuously and creates digital clones of real-world systems.

Up to 90% lower memory and compute footprint, enabling efficient operation across environments. Use it in air-gapped environments.
EVO transforms how organisations deploy and use artificial intelligence. Instead of relying on static datasets or large external AI services, EVO enables intelligence to operate in real-world environments, supporting faster, more reliable decisions and actions with lower infrastructure and operating costs.
AI today does not operate effectively in real-world environments. These are not isolated issues — they are structural limitations in how most AI systems are designed and deployed.
AI systems require large models, repeated retraining and significant compute resources, making them costly to run and scale.
Most AI systems depend on centralised infrastructure and external services, limiting where they can operate.
AI models are trained in advance and updated periodically, preventing them from adapting quickly to changing conditions.
Without clear evidence or traceability, teams cannot rely on AI outputs in critical or regulated environments.
Most AI systems predict outcomes but cannot evaluate the impact of decisions before they are made.
Neuro-symbolic intelligence is a fundamentally different approach. Rather than analysing static datasets alone, intelligence must observe environments as they operate, learn continuously from incoming information and understand how systems behave as conditions change.
AsterMind has developed a new artificial intelligence architecture designed specifically for this purpose.
At the core of AsterMind's architecture is a proprietary neural topology that enables intelligence to learn directly from live environments and construct evolving representations of system behaviour — practical to deploy across cloud, on-premise and distributed environments.
Astermind’s architecture enables a new set of intelligence capabilities designed to understand and analyse complex environments.
These capabilities allow intelligence to operate continuously alongside the systems it observes, learn as environments evolve and evaluate how systems respond to change. continuously alongside the systems they observe.
Learns from live environments rather than relying on static training datasets. As new information is observed, the system refines its understanding. When situations require judgment, EVO uses a human-in-the-loop — every expert decision is fed back into the digital clone, so EVO continuously gets smarter and more autonomous over time.
Constructs evolving representations of how environments behave, capturing relationships, signals and patterns to understand system behaviour as conditions change.
Evaluate scenarios, test conditions and analyse possible outcomes before actions are taken — reducing operational risk.
Astermind's neural topology enables efficient runtime models that require significantly less infrastructure than traditional AI systems. 99% faster execution, 90% smaller models.
Results can be examined, verified and traced back to the signals and relationships that influenced the analysis — supporting governance and compliance.
Based on the changes EVO detects in the environment and the predictions it generates, it can take instant action — triggering alerts, workflows or automated responses the moment a meaningful signal emerges.
AsterMind removes work: less glue code, fewer brittle rules, fewer late-night incidents, faster recovery when things change.
End-to-end AI solutions ready to deploy in your infrastructure based on the AsterMind Platform.
Browser-native, closed-form AI stack for retrieval, reranking, and summarization. Built on ELM/KELM with deterministic processing, Transfer Entropy controls, and millisecond training—fully offline with complete explainability.
Adaptive Data Operations Platform
Maintains semantic and structural stability in living data systems. Four integrated modules — SchemaSense™, Normalize™, DriftGuard™, and Data Reflex™ — form a closed feedback loop for modern data operations, continuously observing, adapting, and responding as data evolves.
Browser-native ELM technology for development teams
Everything you need to know about AsterMind