Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy millisecond-latency perception and decision-making for robots and drones, consuming a fraction of the power of traditional AI.
Autonomous vehicles, drones, and industrial robots are hitting fundamental physical limits. Traditional GPU-based AI stacks demand kilowatts of power and introduce unpredictable latency, making true real-time autonomy in dynamic environments impossible. Neuromorphic computing, inspired by the brain's architecture, is the only viable path forward.
We architect spiking neural networks (SNNs) on hardware like
Intel LoihiorBrainChip Akidato deliver deterministic, sub-10ms inference with microwatt to milliwatt power consumption. This enables continuous operation and complex environmental reasoning where batteries and thermal budgets are constrained.
event-based camera and LiDAR streams with >90% lower energy than convolutional neural networks (CNNs), enabling real-time object classification and tracking for navigation.Move from constrained prototypes to production-ready autonomy. Explore our foundational approach in Neuromorphic Computing AI Integration or see how we optimize for specific hardware in Neuromorphic AI Performance Tuning.
Deploying neuromorphic AI for autonomous systems delivers measurable advantages beyond traditional edge computing. Our development services translate cutting-edge hardware into tangible operational and financial results.
We develop spiking neural networks (SNNs) on hardware like Intel Loihi to achieve deterministic, sub-10ms response times for critical tasks like robotic obstacle avoidance and drone navigation in dynamic environments, enabling true real-time autonomy.
Our neuromorphic hardware-software co-design leverages event-driven computation, reducing power consumption from watts to milliwatts. This extends operational life for battery-powered robots and drones, slashing total cost of ownership.
By enabling complex neural network inference directly at the sensor edge, our solutions minimize the need for constant high-bandwidth data transmission. This cuts cloud compute costs and allows operation in disconnected or low-connectivity scenarios.
Our end-to-service covers spiking neural network development, hardware integration, and performance tuning. We provide a clear path from architectural consulting to a production-ready system, de-risking adoption of neuromorphic technology.
We architect systems that separate perception and decision logic, allowing for incremental upgrades of sensor suites or neural models without full platform redesigns. This protects your investment against rapid advancements in neuromorphic chips and AI algorithms. Learn more about our approach to Neuromorphic System Architecture Consulting.
A clear, phased approach to developing and deploying neuromorphic perception and decision-making systems for autonomous robots and drones, ensuring predictable outcomes and risk mitigation.
| Phase & Deliverables | Proof-of-Concept (4-6 weeks) | Pilot Integration (8-12 weeks) | Production Deployment (Ongoing) |
|---|---|---|---|
Primary Objective | Validate core SNN model feasibility for target task (e.g., object avoidance) | Integrate neuromorphic subsystem with robot/drone platform in controlled environment | Full-scale deployment with reliability, monitoring, and continuous optimization |
Key Activities | Algorithm selection & simulation (Nengo/Lava)Dataset preparation for event-based sensorsBenchmarking on target hardware (e.g., Intel Loihi) | Hardware-in-the-loop testingLatency & power consumption profilingIntegration with ROS 2/autonomy stack | Fleet-wide model deployment & OTA updates99.9% uptime SLA managementPerformance monitoring & drift detection |
Technical Support | Weekly engineering check-ins | Dedicated engineering sprint team | 24/7 dedicated support with 1-hour SLA |
Success Metrics | >90% simulation accuracy on target taskPower consumption estimate < 500mWFeasibility report with go/no-go recommendation | <10ms end-to-end inference latencySuccessful field test in controlled environmentIntegration documentation & API | 99.9% system uptime<5ms latency variance in production>30% reduction in energy vs. traditional edge AI |
Typical Output | Feasibility report with quantified benchmarks and architecture recommendation | Functional prototype integrated on target platform with full documentation | Production-grade neuromorphic AI system with monitoring, governance, and support |
Investment | Starting at $25K | Starting at $75K | Custom annual agreement |
Our neuromorphic AI systems deliver deterministic, ultra-low latency performance for autonomous platforms operating in dynamic, resource-constrained environments. We architect solutions that move beyond simulation to reliable field deployment.
Build next-generation sensor fusion systems for automotive. Integrate neuromorphic processors to process LiDAR point clouds and radar signals with deterministic latency for critical functions like emergency braking and pedestrian detection, meeting stringent automotive safety standards (ISO 26262) and thermal budgets.
Deploy intelligent control systems for collaborative robots (cobots) on manufacturing lines. Neuromorphic AI enables real-time, adaptive force feedback and precise motion planning for delicate assembly tasks, operating reliably in high-electromagnetic-interference (EMI) environments where traditional compute may fail.
Develop resilient autonomous systems for contested environments. Our solutions leverage the inherent noise tolerance and low electromagnetic signature of neuromorphic computing for secure, jam-resistant perception and decision-making in field-deployed ground and maritime robotics.
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 clear answers on timelines, costs, and technical integration for deploying neuromorphic AI in your autonomous robots, drones, or vehicles.
Our standard engagement delivers a functional prototype in 4-6 weeks, with production-ready system integration completed in 8-12 weeks. This includes spiking neural network (SNN) design, hardware-software co-design for platforms like Intel Loihi or BrainChip Akida, and rigorous testing in simulated dynamic environments. For complex multi-agent fleets, timelines scale accordingly with clear milestones.
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.
