Services
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.
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.
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.
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.
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.
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.
Development of adaptive AI algorithms that identify sources of RF interference and automatically reconfigure network parameters or activate countermeasures to maintain communication link integrity.
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.
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.
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.
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.
Implementation of complete MLOps pipelines (using MLflow, Kubeflow) for continuous training, deployment, and monitoring of RF machine learning models in production environments.
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.
How We Work
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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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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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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