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Protect personnel by transforming overwhelming sensor data into actionable, predictive intelligence.
Modern patrols face a dual threat: unpredictable IED emplacement patterns and sensor overload from ground-penetrating radar, drones, and signals intelligence. Manually correlating this data is too slow for real-time threat assessment, creating dangerous gaps in situational awareness.
Our AI systems fuse multi-source sensor data to predict high-risk zones and detect anomalies with >95% accuracy, reducing false positives by 70% and accelerating threat identification from minutes to seconds.
Our AI for Counter-Improvised Explosive Device (C-IED) development delivers measurable improvements in mission safety and operational tempo. We engineer systems that transition from reactive detection to proactive prediction, directly enhancing force protection.
Deploy machine learning models that analyze historical IED attack patterns, terrain data, and local intelligence to generate probabilistic heatmaps of likely future emplacement zones. This enables proactive route planning and area denial, moving from chance discovery to informed avoidance.
Integrate and process data from ground-penetrating radar, electromagnetic induction sensors, and optical systems through a unified AI pipeline. Our models reduce false positives by cross-validating signals, delivering higher confidence alerts to dismounted patrols and convoy protection teams.
Automate the analysis of complex sensor feeds and intelligence reports. Our systems provide clear, prioritized alerts and contextual recommendations, allowing human operators to focus on critical decision-making rather than data sifting, significantly reducing fatigue-induced errors.
Implement real-time, on-vehicle AI that processes feeds from mounted cameras and sensors to identify potential IED indicators (disturbed earth, command wires) at operational speeds. This provides lead vehicles with crucial seconds for evasive action, directly protecting personnel and assets.
Deploy optimized, small-footprint AI models on ruggedized tactical hardware for operation in Disconnected, Intermittent, and Low-bandwidth (DIL) environments. Ensures continuous C-IED detection capability without reliance on vulnerable rear-area data links.
Develop and deploy AI models rigorously tested against data poisoning, evasion attacks, and spoofing techniques using frameworks like MITRE ATLAS. We ensure your C-IED systems maintain high accuracy even when adversaries attempt to degrade or deceive sensor inputs.
Our proven methodology for delivering secure, mission-ready AI systems for C-IED operations, ensuring rapid fielding of initial capabilities while building toward a fully integrated solution.
| Phase | Duration | Key Deliverables | Deployment Scope | Primary Objective |
|---|---|---|---|---|
Phase 1: Foundation & Rapid Prototype | 4-6 Weeks | Proof-of-concept threat pattern analysis model Initial sensor data processing pipeline Secure development environment setup | Lab & Simulation Environment | Validate core ML approach for IED pattern detection and establish technical feasibility. |
Phase 2: Core Model Development & Validation | 8-10 Weeks | Production-grade ML model for emplacement prediction Integrated ground-penetrating radar (GPR) AI analysis module Model validation against historical attack data | Controlled Test Range | Achieve >90% accuracy in controlled tests and secure necessary operational approvals. |
Phase 3: System Integration & Edge Deployment | 6-8 Weeks | Ruggedized edge AI inference appliance Secure API for integration with existing C2 systems On-device model with <100ms latency | Limited User Evaluation (LUE) with a single unit | Demonstrate real-time functionality on representative hardware in a simulated operational environment. |
Phase 4: Pilot Deployment & Operational Assessment | 8-12 Weeks | Full system deployed to a designated operational unit Comprehensive training materials and SOPs Performance analytics dashboard | Pilot Unit Deployment | Gather real-world feedback, measure operational impact, and refine models with live data under strict governance. |
Phase 5: Full-Scale Rollout & Sustainment | Ongoing | Scaled deployment across designated forces Continuous model retraining pipeline 24/7 dedicated technical support & incident response | Enterprise-Wide Deployment | Achieve full operational capability, ensure system resilience, and establish a cycle of continuous AI improvement. |
We engineer AI systems for C-IED with security and reliability as the foundational layer. Our methodology is built on defense-grade standards, ensuring models perform accurately in contested environments and remain resilient against adversarial attack.
We train and fine-tune models within accredited, air-gapped computing environments. Data never leaves sovereign infrastructure, ensuring full compliance with ITAR, EAR, and specific defense contractual data residency requirements.
We proactively attack our own models to find weaknesses. Our red teaming exercises simulate data poisoning, evasion attacks, and sensor spoofing specific to IED detection scenarios, hardening the system against real-world adversarial conditions.
Models are optimized for low-SWaP (Size, Weight, and Power) edge hardware using techniques like quantization and pruning. They are designed to function in DIL (Disconnected, Intermittent, Low-bandwidth) environments with graceful degradation, not total failure.
We provide the full documentation, evidence, and operational monitoring required for Authority to Operate (ATO) processes. Our MLOps pipelines include continuous drift detection and performance validation to maintain accreditation post-deployment.
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 deploying AI for Counter-Improvised Explosive Device detection, from timelines and security to integration and support.
A standard deployment for a production-ready C-IED AI system, from initial data assessment to field-ready model, typically takes 6 to 10 weeks. This includes 2-3 weeks for sensor data pipeline engineering and environment setup, 3-4 weeks for model development and initial training on historical IED data, and 1-3 weeks for integration testing and hardening. For urgent operational needs, we offer accelerated programs that can deliver a minimum viable capability in as little as 4 weeks. Learn more about our rapid deployment process for defense and national intelligence AI.
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.
