Persistent Intelligence for Adaptive AI Systems – Industrial Collaboration and R&D Partners

Qognetix is a UK deep-tech company developing Persistent Intelligence (PI), a biologically inspired computational approach designed to enable intelligent systems to learn continuously, maintain internal state and adapt through experience without relying exclusively on centralised training or repeated model inference.

Conventional AI systems typically rely on models trained using historical datasets, with adaptation often requiring retraining, updates or external computational resources. This can present challenges in dynamic environments where operating conditions change, connectivity is restricted or systems need to adapt locally.

Persistent Intelligence explores an alternative approach, drawing on biological mechanisms including neural dynamics, spiking neural networks, synaptic adaptation and local learning to support intelligence that persists and develops through ongoing interaction with its environment.

We are seeking industrial and research partners interested in evaluating how these capabilities could complement existing AI technologies or enable new forms of adaptive intelligence.

What We Offer

Qognetix has developed substantial foundational technology, including SIBINE, our proprietary intelligence execution environment, supported by a dedicated computational language and development toolchain.

Our technology is designed to support:

• Continuous Local Learning: Adaptation through experience without requiring every learning update to depend on centralised retraining.

• Persistent Intelligence State: Maintaining internal computational state and learned information across successive interactions.

• Biologically Inspired Neural Dynamics: Computational mechanisms drawing on spiking neurons, synaptic behaviour and biological learning principles.

• Adaptive Behaviour: Responding to changing conditions, accumulated experience and evolving operational requirements.

• Hardware-Agnostic Computing: A portable computational approach intended for heterogeneous hardware, from resource-constrained embedded devices to larger computing infrastructure.

• Autonomous Operation: Supporting systems that can operate and adapt locally with reduced dependence on continuous cloud connectivity.

PI can be combined with Qognetix's Trusted Execution (TE) technology, designed to provide runtime governance, operational constraints, execution traceability and intervention capabilities.

Partners Sought

We are particularly interested in connecting with:

• Robotics and Autonomous Systems Developers: Exploring embodied intelligence, adaptive robotics and experience-driven behaviour.

• Industrial AI Developers: Developing adaptive control, predictive maintenance, optimisation and intelligent automation.

• Hardware and Embedded Systems Developers: Investigating local learning and persistent intelligence in constrained computing environments.

• Research Institutions: Universities, Catapults and research groups working in computational neuroscience, neuromorphic computing, spiking neural networks and continual learning.

• Industrial Operators: Organisations in energy, manufacturing, automotive, aerospace, defence and critical infrastructure seeking adaptive and resilient intelligent systems.

Potential Collaboration Areas

We are interested in projects involving:

• Continuous local learning in dynamic operational environments.

• Adaptive robotics and embodied intelligence.

• Persistent intelligence for autonomous and embedded systems.

• Biologically inspired learning and neuromorphic computing.

• Local adaptation under computational, energy or connectivity constraints.

• Integration with existing AI models, sensors and control systems.

• Comparative evaluation against conventional AI approaches.

• Combining PI and TE to investigate adaptive intelligence operating within governed execution boundaries.

Expected Collaboration Outcomes

Our objective is to establish collaborative projects progressing from technical feasibility through independent validation and application-specific demonstrations.

Potential outcomes include feasibility studies, prototype integrations, comparative benchmarking, simulation-based demonstrations, industrial pilots, research publications and collaborative funding applications.

We welcome discussions with organisations forming consortia for BridgeAI, Innovate UK and other UKRI-supported innovation programmes.

Our ambition is to advance AI beyond systems that primarily infer from previously learned patterns towards intelligent systems capable of maintaining experience, learning locally and adapting continuously within their operating environments.