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EVO is an environment intelligence platform designed to analyse complex environments and understand how systems behave. Built on Astermind's environment intelligence architecture, EVO learns continuously from live environments and constructs digital clones of those environments.
EVO constructs digital clones that model the behaviour of the systems being observed. EVO can analyse environments across multiple domains, including data environments and visual environments. Each environment is analysed using specialised intelligence modules known as EVO engines.
EVO enables organisations to analyse complex environments, understand how systems behave and evaluate how those systems respond to change. Built on Astermind's real-time learning approach, EVO allows intelligence to operate continuously alongside the systems it observes. EVO can generate predictions, but extends beyond prediction by evaluating outcomes through simulation.
EVO analyses environments as they operate, allowing organisations to observe system behaviour and identify patterns, signals and anomalies as they emerge.
Potential changes can be evaluated before they are introduced into the environment.
EVO's runtime models are designed to operate efficiently across cloud, on-premise and distributed environments.
Outcomes can be traced back to the signals and relationships that influenced the analysis.
The insights produced by EVO can support downstream systems and operational processes.
EVO works by learning patterns and relationships from incoming data to continuously update digital clones that represent system behaviour
EVO accepts data from a wide range of sources including files, sensor outputs, images, structured datasets and unstructured data.
EVO analyses incoming data to identify patterns, relationships and behavioural signals. As this understanding develops, EVO constructs a lightweight digital clone representing the environment being observed. This clone evolves continuously as new data arrives.
EVO engines execute simulations on the digital clone to analyse how the system behaves under different conditions.
EVO produces results that include the evidence required to validate how the analysis was generated.
Results can trigger alerts, workflows or automated system responses.
Because EVO maintains digital clones, system states can be replayed and analysed to understand behaviour under different conditions.
EVO learns by observing real-world environments and continuously refining its understanding of how those environments behave.
Instead of relying on models trained solely on historical datasets, EVO studies patterns and relationships as data arrives and constructs digital clones that represent how the environment behaves.
The digital clone evolves continuously as new data arrives, allowing EVO to maintain an up-to-date understanding of the system it is analysing.
EVO engines execute simulations to evaluate scenarios, analyse behaviour and produce validated results that can support downstream actions.
A fundamentally different approach to intelligence — built on real-time learning, digital clones, and simulation
Many AI platforms rely on large models, repeated API calls and compute-intensive infrastructure. EVO uses a highly efficient neural architecture that dramatically reduces the resources required.
EVO's architecture requires significantly less compute, memory and infrastructure than traditional AI systems.
Built on Astermind's real-time learning approach, EVO allows intelligence to evolve as systems and conditions change, reducing dependence on model retraining cycles.
EVO evaluates potential outcomes through simulation, allowing organisations to evaluate decisions before taking action and reducing operational risk.
EVO can run across cloud platforms, on-premise infrastructure and fully air-gapped environments, allowing organisations to deploy intelligence in locations where traditional AI systems cannot operate.
EVO constructs digital clones that represent how systems behave, enabling deeper understanding of relationships, dependencies and behaviour across complex environments.
Each EVO engine is designed to analyse a specific type of environment while operating within the EVO platform's environment intelligence architecture. This allows different forms of intelligence to operate within the same platform while sharing the same learning framework, simulation capability and evidence model.
Analyses environments composed of structured and unstructured data. These environments may include operational systems, application platforms, data pipelines, infrastructure telemetry or other complex data-driven systems.
Analyses environments observed through visual data such as images and video. These environments may include visual monitoring systems, navigation systems, safety systems or any environment where system behaviour is observed through cameras or imaging devices.
Deploy where your systems operate — without being constrained by infrastructure or external AI services
Operate within cloud environments alongside existing infrastructure and AI services.
Run within on-premise infrastructure, directly on devices and within operational systems.
Operate within fully isolated environments without external AI services or internet connectivity.
Operate alongside existing AI platforms as an optimisation layer coordinating with external systems.
EVO takes a fundamentally different approach, built on Astermind's real-time learning paradigm
Models trained on historical datasets
Learns continuously from live environments
Generates predictions from patterns
Evaluates outcomes through simulation
Large GPU infrastructure required
Up to 90% lower memory and GPU usage
Cloud-dependent operation
Cloud, on-premise or fully air-gapped
Retraining required when conditions change
Adapts continuously without retraining
Opaque decision outputs
Transparent, traceable and verifiable results
Faster Execution
Than typical API-based model calls
Smaller Models
Than typical LLM architectures
Fewer Model Calls
When interacting with existing AI models
Lower GPU Usage
No large GPU infrastructure required
Common questions about the AsterMind AI EVO Platform