Reference Architectures
Illustrative deployment models designed to show how the Navigato AI platform can be applied to real infrastructure challenges. These are reference configurations, not deployed customer implementations.
Illustrative Deployment Models — these architectures are designed to demonstrate platform capability. They are not deployed customer implementations or proven production systems.
EV Infrastructure Intelligence
National eMobility Platform
A reference architecture for a national fuel-station or retail network transforming its physical estate into a scalable eMobility platform. Designed for organisations with 50–500+ existing sites seeking to deploy EV charging at scale across road corridors and urban locations.
Business Context
- Organisation type: national fuel retailer or large CPO
- Sites: 100–500 locations
- Timeline: 18–36 month phased rollout
- Starting point: GIS audit and Digital Twin model
Illustrative KPIs
Model-based assumptions only
- Target utilisation: 15–35% (model assumption)
- Revenue per charger: depends on location, pricing and demand
- System uptime: design target >98%
Financial projections are model-based and depend on site selection, grid access, charging demand and pricing strategy.
Key Integrations
Architecture Layers
Phased Deployment Model
Proposed architecture — subject to site assessment, grid access and regulatory compliance.
Discuss an eMobility PilotSmart Transformers & Substations
Smart Transformer Vendor Platform
A reference architecture for a mid-to-large transformer manufacturer launching a digital lifecycle platform across its installed base. Designed to create a white-label Digital Passport and connected asset service that generates recurring revenue without requiring hardware replacement.
Business Context
- Organisation type: transformer or substation manufacturer
- Installed base: 1,000–50,000+ assets (illustrative range)
- Starting point: Digital Passport pilot on new production units
- Growth path: retrofit existing installed base progressively
Illustrative Value Metrics
Model-based assumptions only
- Target digital attachment rate: 40–80% of new units in year 1 (illustrative)
- Recurring revenue per asset: depends on service tier and pricing
Revenue projections depend on market pricing, adoption rate and service tier uptake.
Deployment Note
Manufacturer brand, domain and customer relationships remain primary throughout. Data ownership is contractually defined.
Architecture Components
Four-Level Product Rollout
Illustrative model — commercial terms, data ownership and integration scope defined in pilot agreement.
Discuss a Smart Asset PilotAdaptive Traffic & Urban Mobility
Adaptive Traffic Management Platform
A reference architecture for a European city deploying AI-based adaptive traffic management across a priority corridor or district. Based on European urban-pilot principles and applicable to cities with different infrastructure maturity, transport systems and regulatory environments.
Business Context
- Organisation type: municipality or metropolitan transport authority
- Pilot scope: 5–50 intersections
- Starting point: Digital Twin and baseline simulation
- Growth path: corridor → district → citywide
Governance Requirements
Modules in Use
Deployment Phases
Illustrative KPI Targets
Model-based — validated during pilot phase
- Average journey time reduction: depends on baseline and corridor characteristics
- Public transport punctuality: improvement target defined in pilot scope
Performance projections are established during the Digital Twin baseline phase and validated with field data during the human-approved pilot. No production claims are made prior to field validation.
Representative architecture — applicable to cities with different infrastructure maturity and regulatory environments.
Discuss a City PilotShared Foundation
All three architectures share one platform core
Whether the asset is an EV charger, transformer or traffic signal, the same six-layer platform stack provides connectivity, intelligence and governance.
Physical Assets
EV chargers, transformers, traffic signals and sensors — the physical estate the platform connects.
Data & Connectivity
OCPP, IoT sensors, edge gateways and APIs ingesting real-time signals from every asset.
Digital Twin
High-fidelity virtual models representing each asset, site and network — updated continuously.
AI Intelligence
Predictive analytics, anomaly detection, demand forecasting and adaptive optimisation engines.
Operations
NOC dashboards, field operations tooling, customer portals and management reporting.
Governance & Evidence
Full audit logs, human-approval workflows, rollback controls and regulatory evidence packs.
About These Reference Architectures
These reference architectures are illustrative deployment models created to demonstrate how the Navigato AI platform can be applied to infrastructure challenges in each sector. They are not case studies of deployed customers, signed contracts or live operational systems. Physical deployment depends on site assessment, regulatory compliance and applicable permits. Financial projections are model-based and subject to real-world conditions. Navigato AI acts as a technology integrator and platform provider.
Next Steps
Discuss which reference architecture fits your organisation
We work with organisations in each sector to scope, validate and pilot deployments based on real site data and operational context.