Persistent Intelligence
for the Physical World
Sensing-native. Edge-native. Sovereign by design.
We build the complete PSI stack for industrial monitoring, environmental intelligence, and sovereign infrastructure. The shift is from dataset -> stateless inference to sensing -> time -> memory -> persistent intelligence.
Living Memory Architecture
Nature's blueprint for adaptive intelligence
The Sensing Turn
We are moving beyond scale-first cloud AI toward Persistent Sensing Intelligence: systems that are present, persistent, and physically grounded.
What we do now
- • Build event-driven inference and temporal memory integration
- • Run edge-native pilots where cloud AI is architecturally unviable
- • Convert research into deployable systems through Verkko.ai
Who it is for
- • Industrial monitoring and predictive maintenance programs
- • Precision agriculture and environmental intelligence teams
- • Sovereign infrastructure and defence-adjacent organisations
What is Verkko.ai?
Verkko Robotics builds the core technology and deployment architecture. Verkko.ai is the product layer where selected capabilities are packaged for real-world use.
Visit Verkko.aiMission Statement
Build intelligence that computes only when the world changes and accumulates operational memory over months and years.
Collapse sensing, inference, and memory into one edge architecture that is private, resilient, and sovereign by default.
Core Goal
Deliver Persistent Sensing Intelligence as a real deployment stack, not a lab-only concept: event sensors, VOLTAIC inference, HSC v2 Living Memory consolidation, and adaptive metaplasticity.
Emergent Intelligence
Edge-native PSI systems
Architectural Sovereignty
Data stays where it is generated
Why This Direction Matters
The next decade requires intelligence that is present in the world, not rented from a distant API. PSI targets energy efficiency, low-latency action, and long-term memory under real physical constraints.
Architecture Shift
The key shift is from stateless query systems to intelligence that continuously senses, encodes time, consolidates memory, and adapts in-place.
Living Memory Architecture
Where sensing, inference, and memory operate as one stack
Memory Manifolds
HSC v2 Living Memory
Multi-timescale memory consolidation supports stable continual learning, with adaptive forgetting and on-device persistence across long operational windows.
Spiking Neural Networks
VOLTAIC Inference Layer
Event-driven spiking inference preserves temporal structure, supports criticality-aware sensitivity, and computes proportionally to activity rather than model size.
Real-time Plasticity
Adaptive Metaplasticity
Plasticity is tuned continuously from live performance signals, enabling local adaptation without task boundaries and without cloud retraining loops.
Biological-Digital Bridge
Sensing-Native Computing
Biological principles are used as engineering specifications for sparse, temporal, stateful systems that are physically grounded and edge deployable.
From Scale to Presence
PSI is designed for systems that must operate with low energy, low latency, and high autonomy. It serves domains where cloud dependence is expensive, unreliable, or strategically unacceptable.
Technology
The stack is designed for environments where cloud AI cannot meet energy, latency, or sovereignty constraints.
Persistent Sensing Intelligence
A system-level architecture that co-locates sensing, inference, and memory directly at the edge
VOLTAIC Spiking Inference
Event-driven temporal inference with spike-frequency adaptation and criticality-aware dynamics
HSC v2 Living Memory
Per-parameter memory consolidation with earned persistence — continual learning across domains without catastrophic forgetting
Sovereign Edge Deployment
On-device adaptation and local processing for privacy, resilience, and architectural sovereignty
Verkko Equilibrium Engine
VEEA principled system for managing what the model retains and forgets across knowledge domains — ensuring stable, balanced learning without manual tuning of replay schedules.
Closed PSI Loop
Event sensors feed spiking inference, which feeds memory consolidation and metaplastic adaptation: Sensing → VOLTAIC → HSC v2 → adaptation → action → feedback.
Three Horizon Roadmap
Horizon 1 validates algorithmic foundations. Horizon 2 proves field deployments in industrial and environmental settings. Horizon 3 scales production and prepares personal PSI at wearable class power budgets.
Applications
Deployment domains where cloud-dependent AI is structurally limited and edge-native PSI creates immediate value.
Edge-Native Intelligence
Where every event is processed locally, every timescale is remembered, and every deployment gains presence
Neuromorphic
Hardware Integration
Embodied AI
Real-world Learning
Industrial Monitoring and Predictive Maintenance
Edge systems that accumulate machine-specific experience and detect weak failure signals before breakdown.
Precision Agriculture and Environmental Sensing
Milliwatt, low-connectivity deployments for long-duration monitoring in remote and resource-constrained conditions.
Sovereign Infrastructure and Personal PSI
On-device intelligence for critical systems today and private personal persistent intelligence as hardware matures.
Deployment Strategy
PSI is deployed first where cloud AI is unviable, then scaled toward personal and sovereign intelligence. The architecture is designed to move from industrial footholds to long-term infrastructure and personal systems.
Current Trajectory
Where We Are
The PSI stack is not a concept — it is being built and measured. The foundation is in place. Field deployment is the next horizon.
355M
Spiking parameters
Foundation model built on binary spike dynamics across all layers — no smooth activations.
HSC v2
Living Memory
Per-parameter memory consolidation demonstrated across sequential multi-domain training.
VEE
Verkko Equilibrium Engine
Principled system for managing what the model retains and forgets across knowledge domains.
H2
Field deployment pilots
Industrial and environmental intelligence pilots — active preparation underway.
The Destination
70B+ parameters.
>85% sparsity.
A spiking model at this scale — with the majority of computation silent at any given moment — is where the energy and latency advantages of spike-native architecture become structural rather than marginal. This is the horizon that makes personal and sovereign PSI physically real.
70B+
Target parameters
>85%
Spike sparsity
The algorithmic foundations are validated. The next step is proving PSI in the field — where cloud AI cannot follow. If you are building in industrial monitoring, environmental intelligence, or sovereign infrastructure, this is the moment to engage.
Partner With Verkko
Build edge-native, sovereign, and persistent intelligence deployments with our research and engineering team.
Research and Engineering
Pisa, Italy
Largo Spadoni 1
Get In Touch
research@verkko.ai
Location
Faversham, UK and Pisa, Italy