Services
Radio Frequency (RF) Machine Learning
Radio Frequency (RF) Machine Learning
Application of deep learning to raw electromagnetic data for RF signal classification in congested environments, dynamic spectrum sharing management, and predictive operations in cellular networks including 6G. Sub-services include RFML for 6G spectrum awareness, AI-native telecommunications network automation, deep learning for wireless signal classification, and airborne signals intelligence ML.
RF Signal Intelligence AI Consulting
Expert consulting to design and deploy AI systems that automatically intercept, classify, and geolocate RF signals for national security and defense applications, focusing on real-time analysis in contested environments.
RF Anomaly Detection AI Services
Development of machine learning models to detect and classify anomalous RF signals indicative of jamming, spoofing, or equipment failure, providing early warning for critical infrastructure and network security.
Predictive Cellular Network Operations AI
Implementation of AI systems that forecast network congestion, predict cell site failures, and automate capacity planning for telecom operators, reducing operational costs and improving service quality.
RFML Model Development and Training
End-to-end service for developing, training, and validating custom deep learning models (CNNs, Transformers) on proprietary RF datasets for specific tasks like modulation recognition and emitter identification.
Edge AI for RF Signal Processing Engineering
Deployment of optimized RFML inference models on edge devices (SDRs, UAVs) using TensorFlow Lite and NVIDIA Jetson, enabling low-latency signal analysis without cloud dependency for tactical and IoT applications.
AI-Powered RF Interference Mitigation
Development of adaptive AI algorithms that identify sources of RF interference and automatically reconfigure network parameters or activate countermeasures to maintain communication link integrity.
RF Digital Twin Development
Creation of high-fidelity, AI-driven digital twins of RF environments (cities, battlefields) that simulate propagation, traffic, and interference to test network configurations and predict performance.
Generative AI for RF Signal Synthesis
Leveraging GANs and diffusion models to generate synthetic RF waveform datasets for training robust ML models, overcoming data scarcity and privacy constraints in sensitive domains.
AI for RF-Based Positioning Systems
Engineering of machine learning solutions that use RF signal fingerprints (Wi-Fi, Bluetooth, Cellular) for high-accuracy indoor and urban positioning, surpassing traditional GPS limitations.
RFML for Electronic Warfare Systems
Specialized development of AI models and processing pipelines for electronic support (ES), attack (EA), and protection (EP) applications, including threat library management and reactive jamming.
RFML MLOps and Lifecycle Management
Implementation of complete MLOps pipelines (using MLflow, Kubeflow) for continuous training, deployment, and monitoring of RF machine learning models in production environments.
Multi-modal RF Data Integration Services
Architecture and engineering of systems that fuse RF I/Q data with other modalities (EO/IR, GIS) using multimodal AI to provide comprehensive situational awareness for intelligence and surveillance.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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