InfraSense Platform
A sensing-aware, latency-guaranteed AI inference layer for the 5G/6G RAN — making latency a first-class, enforced property of every inference request, with intent-driven ISAC and SMO-level conflict arbitration. Running today on NVIDIA GPU infrastructure with measured evidence. US provisional patent filed.
InfraSense
InfraSense is a working demonstrator of a dedicated AI inference layer for the 5G/6G RAN. Every inference request declares a latency class; the orchestrator places it only on infrastructure tiers that can honor that class — and explicitly refuses a request rather than silently misrouting it. Companion innovations add A1 SensingIntent policies for intent-driven ISAC and an SMO Global Agent for priority-based conflict arbitration. All three components run today, end-to-end, on NVIDIA GPU infrastructure with measured (not simulated) latency evidence.
- Declared latency classes with refusal semantics
- A1 SensingIntent policies for intent-driven ISAC
- SMO Global Agent priority arbitration
- Measured: 1.3–1.8× dedicated GPU variance vs order-of-magnitude cloud swings
The sensing layer every AI network is missing
Three structural shifts in telecom AI — agentic operations, sovereign factories, and distributed inference economics — all converge on the same gap. InfraSense closes it.
Agents reason about the physical world, not just KPI telemetry
"An agent reasoning about coverage degradation needs to know that a construction crane appeared 200 metres from the cell site, that pedestrian density increased 40%, and that the interference pattern shifted at 14:23 UTC. That data does not come from the core network."
InfraSense delivers that environmental context to the O-RAN near-RT RIC via xApps — making it available to the reasoning models and agentic workflows running on top. The result is an autonomous network that understands what is happening in the physical world and acts accordingly.
Read the full analysisEdge AI applications need to understand the physical environment they operate in
"A model that controls a logistics robot needs to know the current state of that warehouse: where the humans are, what the RF environment looks like, whether the loading dock is congested. That data comes from sensing, not from the core network."
InfraSense addresses two of the three capabilities sovereign AI factories consistently underestimate: real-time environmental sensing and network-aware inference routing. Our xApps use live RF data to optimise job placement across the distributed compute fabric — on-premises, within jurisdiction.
Read the full analysisRF-aware routing: −23% p99 latency, +18% GPU utilisation
"Most AI grid architectures have good visibility into compute. They have poor visibility into the network conditions that determine whether a routing decision will actually achieve its latency target. This is where RF sensing changes the equation."
InfraSense provides real-time RF visibility — interference patterns, congestion indicators, propagation anomalies — at the granularity of individual cells and sectors. Fed into the inference routing layer, it enables decisions based on actual network conditions. In tier-1 operator testing: p99 latency down 23%, GPU utilisation up 18%.
Read the full analysisFollowing the conversation on LinkedIn
OranSense
AI-Driven 5G/6G Sensing Platform
Why agentic AI networks fail without sensing fidelity
"An agent that cannot sense the network it is routing across is not autonomous — it is guessing. InfraSense closes that gap at the RAN edge."
NVIDIA's agentic AI research assumes the inference layer has reliable visibility into network state. InfraSense provides exactly that — real-time RF sensing fed directly into the agent decision loop.
Read: Agentic AI & Autonomous NetworksOranSense
AI-Driven 5G/6G Sensing Platform
The sovereign AI factory needs a sensing layer
"Sovereign AI factories are being built to process data at national scale. The RAN is the first mile of that data pipeline — and right now it is blind."
NVIDIA's sovereign AI factory model positions telcos as AI infrastructure operators. InfraSense turns the RAN into the sensing foundation that makes that infrastructure viable.
Read: Sovereign AI Factory & TelcoOranSense
AI-Driven 5G/6G Sensing Platform
RF-aware routing: −23% p99 latency, +18% GPU utilisation
"Most AI grid architectures have good visibility into compute. They have poor visibility into the network conditions that determine whether a routing decision will actually achieve its latency target."
InfraSense provides real-time RF visibility — interference patterns, congestion indicators, propagation anomalies — at cell and sector granularity. In tier-1 operator testing: p99 latency down 23%, GPU utilisation up 18%.
Read: AI Grid TokenomicsOranSense
AI-Driven 5G/6G Sensing Platform
The intelligence layer transforming 5G into a sensor network
"AI-RAN is not just about optimising the radio. It is about making the radio a first-class data source for every AI workload running above it."
OranSense InfraSense implements the AI-RAN vision end-to-end — from NVIDIA Aerial SDK GPU-accelerated baseband processing through O-RAN xApp orchestration to SMO-level policy arbitration.
Read: AI-RAN Intelligence LayerNVIDIA AI Factory
CUDA-accelerated RAN software combining OranSense AI frameworks with NVIDIA Aerial and H100/A100 GPU acceleration — enabling up to 40% TCO reduction and unlocking new revenue streams for telcos through AI-RAN monetisation.
- Up to 40% TCO reduction
- <1ms inference latency
- H100/A100 GPU accelerated
- AI-RAN monetisation
Inference Layer
NVIDIA NIM microservices deployed at the RAN edge — no cloud round-trip required. Runs optimised AI models on NVIDIA Jetson, H100, and Intel Xeon for RF classification, anomaly detection, and crowd analytics.
- NVIDIA NIM microservices
- Jetson & H100 optimised
- Sub-ms model serving
- CUDA-X accelerated
AODT — Digital Twin
Artifact-Oriented Digital Twin powered by NVIDIA Omniverse. Simulates the full RF environment in photorealistic 3D — enabling what-if planning, autonomous network optimisation, and xApp validation before live deployment.
- NVIDIA Omniverse powered
- Photorealistic RF simulation
- xApp validation sandbox
- Autonomous optimisation
Edge Orchestration
NVIDIA Dynamo-powered inference orchestration across the RAN-to-cloud continuum. Routes AI workloads to the optimal compute node — Jetson edge, regional H100 cluster, or DGX Cloud — based on latency and cost.
- NVIDIA Dynamo routing
- RAN-to-cloud continuum
- xApp & rApp support
- O-RAN compliant
OranSense Console
Unified dashboard for monitoring NVIDIA GPU utilisation, NIM model performance, network health, and sensing KPIs across your entire 5G deployment — with REST API and webhook integrations.
- GPU & NIM monitoring
- Real-time KPI dashboards
- Multi-site management
- REST API & webhooks
Solutions
Purpose-built configurations of the OranSense platform, each powered by NVIDIA Aerial and CUDA-X inference.
AI-RAN
AI-native Radio Access Network built on NVIDIA Aerial SDK — embedding autonomous intelligence directly into the RAN for real-time spectrum optimisation, interference management, and new revenue generation.
6G AI Grid
Next-generation 6G infrastructure with AI-native air interface design, sub-terahertz sensing, and NVIDIA GPU-distributed intelligence across the entire network fabric.
Omniverse DSX Twin
Digital twin of the RF environment powered by NVIDIA Omniverse — photorealistic simulation, what-if planning, and autonomous network optimisation using physically-based OpenUSD modelling.
Running xApps
Deploy and manage O-RAN xApps and rApps at scale on NVIDIA GPU infrastructure — from spectrum analytics and interference coordination to predictive handover and energy saving.
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