Traditional GPS is unreliable indoors and in dense urban canyons. Our AI systems analyze ambient RF signal fingerprints—from Wi-Fi, Bluetooth, and Cellular networks—to deliver sub-meter positioning accuracy without dedicated infrastructure.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy machine learning systems that use ambient RF signals for high-accuracy location where GPS fails.
Traditional GPS is unreliable indoors and in dense urban canyons. Our AI systems analyze ambient RF signal fingerprints—from Wi-Fi, Bluetooth, and Cellular networks—to deliver sub-meter positioning accuracy without dedicated infrastructure.
We engineer deterministic location intelligence from probabilistic signal data, enabling new applications in logistics, smart buildings, and security.
Wi-Fi/BLE fingerprinting models, cellular signal triangulation AI, and hybrid sensor fusion algorithms.This service is part of our broader Radio Frequency (RF) Machine Learning pillar, which includes solutions for RF Signal Intelligence AI Consulting and Predictive Cellular Network Operations AI. For enterprises requiring data sovereignty, explore our Sovereign AI Infrastructure Development services.
Move beyond basic location services. Our AI-driven RF positioning systems deliver measurable improvements in operational efficiency, user experience, and security, directly impacting your bottom line.
Deploy systems that achieve decimeter-level accuracy using existing Wi-Fi, Bluetooth, and cellular signals, eliminating the need for costly, dense beacon networks. This enables precise asset tracking in warehouses, navigation in complex facilities, and enhanced location-based services.
Gain a live digital twin of asset and personnel movement. Integrate positioning data with enterprise systems like ERP or IWMS for automated workflows, such as triggering maintenance when equipment enters a zone or optimizing staff deployment based on real-time congestion.
Power hyper-contextual applications, from turn-by-turn indoor navigation in airports to personalized retail offers triggered by precise in-store location. This drives higher customer engagement, dwell time, and conversion rates compared to zone-based Bluetooth beacons.
Our AI models adapt to environmental changes and new signal sources, reducing long-term maintenance. Leverage existing enterprise wireless infrastructure, avoiding vendor lock-in and enabling scalable deployment across global sites without hardware overhauls.
A clear breakdown of the phased development process for an RF-based positioning system, outlining key milestones, deliverables, and typical timeframes.
| Phase & Key Activities | Timeline | Primary Deliverables | Client Involvement |
|---|---|---|---|
Phase 1: Data Assessment & Feasibility Study • RF environment analysis & data source audit • Positioning accuracy target definition • Initial algorithm selection (fingerprinting, AoA, etc.) | 2-3 weeks | Feasibility Report & Technical Specification Data Collection & Labeling Strategy High-level System Architecture | Provide access to site/environment data Collaborate on accuracy & coverage requirements |
Phase 2: Model Development & Training • Custom RF fingerprinting dataset creation • Development & training of CNN/Transformer models • Initial validation in simulated environment | 4-6 weeks | Trained Positioning Model (v1.0) Model Performance Report (Accuracy, Latency) Training Pipeline & Documentation | Review performance metrics Provide feedback on real-world constraints |
Phase 3: Edge Deployment & Integration • Model optimization for target edge hardware (Jetson, SDR) • Integration with client's existing infrastructure • Development of positioning API/ SDK | 3-4 weeks | Deployable Edge Inference Module Integration Guide & API Documentation Performance Benchmarks on Target Hardware | Provide test hardware/device access Support integration testing with existing systems |
Phase 4: Pilot Deployment & Validation • Limited-scale deployment in target environment • Real-world accuracy & reliability testing • Iterative model refinement based on field data | 4-5 weeks | Pilot Deployment Report with KPIs Refined Positioning Model (v2.0) Operational Runbook & Monitoring Dashboard | Facilitate site access for deployment Participate in KPI review and validation |
Phase 5: Production Scaling & MLOps • Full-scale deployment orchestration • Implementation of MLOps pipeline for continuous training • Final security & compliance review | 3-4 weeks | Production-Ready RF Positioning System Complete MLOps Pipeline (using MLflow/Kubeflow) System Handoff & Knowledge Transfer | Coordinate with IT/ops teams for rollout Final acceptance testing and sign-off |
Ongoing: Support & Evolution • 99.9% Uptime SLA for inference service • Optional model retraining & performance tuning • Access to expert support & updates | Ongoing | Monthly Performance & Usage Reports Quarterly Model Retraining Cycles Priority Technical Support | Provide feedback on system performance Collaborate on roadmap for new features |
Our RF-based positioning systems deliver centimeter-level accuracy where GPS fails, enabling transformative applications across industries. We engineer solutions that integrate directly with existing Wi-Fi, Bluetooth, and cellular infrastructure.
Enable precise indoor tracking of medical equipment, patients, and staff. Implement geofenced alerts for restricted areas or patient wander management. Our privacy-preserving systems use anonymized signal fingerprints, avoiding invasive cameras or wearables.
Key Outcome: Decrease equipment search time by 70% and enhance patient safety protocols.
Map anonymous customer foot traffic with high fidelity to analyze dwell times, popular pathways, and queue lengths. Integrate with POS data to measure campaign effectiveness and optimize store layouts without infringing on individual privacy.
Key Outcome: Increase conversion rates by analyzing high-intent zones and reducing checkout wait times.
Guide emergency personnel through complex, low-visibility environments like airports, stadiums, and subway systems. Our systems provide reliable positioning for coordination and asset deployment when traditional networks are degraded or overloaded.
Key Outcome: Reduce incident response time in large facilities by providing reliable turn-by-turn navigation to the precise incident location.
Provide blue-force tracking and situational awareness for dismounted operators in GPS-denied environments. Our systems are designed for low-SWaP (Size, Weight, and Power) deployment and resilience against jamming and spoofing, a core competency from our work in RFML for Electronic Warfare Systems.
Key Outcome: Enhance team coordination and mission success rates in complex urban and indoor operational theaters.
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.
Common questions from CTOs and engineering leads evaluating AI for RF-based positioning systems. Answers are based on our experience delivering 50+ projects in wireless signal processing.
Our AI models typically achieve sub-meter to 3-meter accuracy indoors, surpassing traditional fingerprinting by 40-60%. For urban canyon environments, we deliver 5-10 meter accuracy, a significant improvement over standard GPS which can have errors of 15-30 meters. Accuracy depends on signal density (Wi-Fi/Bluetooth APs, cellular towers) and the quality of the training dataset. We use ensemble models combining CNNs for spatial feature extraction with transformer architectures for temporal sequence analysis of signal strength variations.
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.
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