Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Expert AI consulting to detect, classify, and geolocate hostile signals in real-time, contested electromagnetic spectrum.
Modern battlefields and critical infrastructure are saturated with signals. Your team faces a needle-in-a-haystack problem: isolating hostile emitters from dense civilian and commercial RF traffic. Manual analysis is too slow; traditional rule-based systems lack adaptability.
Our consulting delivers AI systems that provide actionable signal intelligence in under 500ms, transforming raw I/Q data into a real-time threat picture.
We architect systems for air-gapped, tactical edge deployment using optimized frameworks like TensorFlow Lite and NVIDIA Jetson, ensuring operation without cloud dependency. This capability is foundational for Electronic Warfare (ES/EP) and secure battlefield communications, directly supporting initiatives like AI for geospatial intelligence analysis and autonomous defense systems. Move from reactive monitoring to predictive, AI-driven spectrum dominance.
Our consulting delivers measurable improvements in signal intelligence operations, from accelerated threat identification to automated, real-time decision support in contested environments.
Deploy deep learning models (CNNs, Transformers) that automatically intercept and classify complex RF modulations with >95% accuracy in under 100ms, enabling immediate threat assessment and response.
Implement AI-driven TDOA/FDOA and fingerprinting techniques to geolocate signal sources with high precision, reducing manual analysis time and enabling rapid targeting or neutralization.
Engineer and optimize models for deployment on ruggedized edge hardware (NVIDIA Jetson, SDRs) ensuring continuous intelligence, surveillance, and reconnaissance (ISR) capabilities in disconnected environments.
All model development, training, and validation occurs within sovereign, air-gapped infrastructure, ensuring compliance with defense regulations and protection of sensitive signal data.
Build high-fidelity AI-driven simulations of RF battlespaces to test network configurations, predict adversarial actions, and train models on synthetic yet realistic data, de-risking field deployment.
Our phased methodology ensures rapid, low-risk progression from concept to operational AI system. Each phase delivers concrete value and builds toward your complete RF signal intelligence capability.
| Phase & Deliverables | Discovery & Strategy | Proof of Concept (PoC) | Pilot Deployment | Full-Scale Production |
|---|---|---|---|---|
Primary Objective | Define scope, data strategy, and success metrics | Validate core AI model accuracy on your data | Integrate AI into a live, limited environment | Deploy hardened, scalable system across all targets |
Key Activities | Threat landscape analysis, data readiness assessment, architecture design | Custom model development/tuning, baseline performance testing | Real-time pipeline integration, operator feedback loops, SLA definition | System hardening, full MLOps automation, comprehensive training |
Typical Duration | 2-3 weeks | 4-6 weeks | 6-8 weeks | 8-12 weeks |
Model Development | Architecture blueprint | Working prototype (e.g., CNN/Transformer for modulation ID) | Production-ready model with validation | Federated/continuous learning pipeline |
Infrastructure Output | Target architecture document (cloud/edge/hybrid) | Containerized inference service | Kubernetes-managed pilot cluster | Full AI Supercomputing and Hybrid Cloud Architecture with 99.9% SLA |
Security & Compliance | Risk assessment & threat modeling | Initial Confidential Computing for AI Workloads design | Air-gapped/secure deployment validation | Full accreditation support (e.g., NIST RMF, Sovereign AI Infrastructure compliance) |
Team Involvement | Your SMEs + Our Architects | Your Data Engineers + Our ML Engineers | Your DevOps + Our MLOps Engineers | Your Full Ops Team + Our Sustaining Engineers |
Success Metrics Defined | Technical & operational requirements document |
| Latency <100ms, uptime >99% in pilot zone | Full operational capability (FOC) acceptance |
Investment Range | $15K - $30K | $50K - $100K | $100K - $250K | Custom (based on scale) |
Next Step Trigger | Approval of technical design | PoC performance meets/exceeds targets | Pilot meets operational KPIs | System handover & support contract |
Our consulting delivers production-ready AI systems that transform raw electromagnetic data into actionable intelligence, enabling decisive advantage in contested environments. We focus on measurable improvements in detection speed, classification accuracy, and operational autonomy.
Deploy deep learning models (CNNs, Transformers) that automatically intercept and classify complex RF signals in under 100ms, even in dense, contested spectrum. We deliver systems with >95% accuracy for modulation recognition and specific emitter identification, enabling rapid threat assessment.
Engineer AI-powered systems that fuse Time Difference of Arrival (TDoA) and Frequency Difference of Arrival (FDoA) data with geospatial context to geolocate RF emitters with high precision. Our solutions reduce positional error by over 60% compared to traditional methods, critical for dynamic targeting and surveillance.
Build AI systems that forecast spectrum occupancy and predict adversarial behavior, enabling proactive dynamic spectrum sharing and electronic protection. This transforms operations from reactive to predictive, optimizing communication resilience and denying adversary use of the spectrum.
Optimize and deploy lightweight RFML models on ruggedized edge hardware (NVIDIA Jetson, SDRs) for low-latency, offline signal intelligence at the tactical edge. We ensure models operate with <2W power draw and maintain high accuracy without cloud dependency, enabling dismounted and airborne operations.
Architect systems that correlate and fuse RF signal intelligence (SIGINT) with data from other intelligence sources (GEOINT, IMINT) using multimodal AI. This creates a unified operational picture, dramatically improving situational awareness and reducing analyst cognitive load for faster decision cycles.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers on timelines, security, and outcomes for deploying AI-driven RF signal intelligence systems in contested environments.
Our process follows a structured 4-phase methodology designed for national security applications. Phase 1 (2-3 weeks) involves requirements analysis and data assessment. Phase 2 (3-4 weeks) focuses on custom RFML model development and validation. Phase 3 (2-4 weeks) is dedicated to edge deployment and integration with your existing SIGINT platforms. Phase 4 includes 90 days of post-deployment support and model monitoring. Most projects move from concept to operational capability in 8-12 weeks.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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.
The first call is a practical review of your use case and the right next step.
