Edge AI Robotics
Compute, Perception and On-Device Navigation for Warehouse AMRs
- Robots in Pilot Zone
- 20
- On-Robot NPU (INT8)
- 4 TOPS
- Obstacle-Classification Accuracy
- 98.5%
- Bench-tested
Specialised engineering
Edge AI Development Services
Rapid Circuitry delivers edge AI development for embedded products that need inference on the device or gateway rather than in the cloud. We run a feasibility study on your target hardware, port and quantize the model for LiteRT (TensorFlow Lite), ONNX Runtime or TinyML targets, integrate it with the hardware and firmware, and support field acceptance, with latency, memory and power measured on the deployment device.
Quick answer
- What we build
- On-device machine-learning inference for embedded products: a feasibility study on your target hardware, model porting and quantisation for LiteRT (TensorFlow Lite), ONNX Runtime or TinyML targets, integration with the hardware and firmware, and field acceptance.
- Who it is for
- Product teams that need inference on the device or gateway, for latency, privacy, connectivity or running-cost reasons, rather than in the cloud.
- Typical timeline
- 8–16 weeks
- Typical cost
- Quoted per project as a fixed-scope proposal. For typical ranges, see our hardware development cost benchmarks.
- Tools and technologies
- LiteRT (TensorFlow Lite), ONNX Runtime, OpenVINO, Edge TPU/NPU
Every project is quoted as a fixed-scope proposal within 3–5 business days of a discovery call. Discuss your product
Capabilities
Edge AI development services we deliver
Real-time neural processing
Low-latency inference on the device, measured end to end on the target: from under 10 ms for small sensor models to tens of milliseconds for vision.
TinyML & model optimization
Hardware-accelerated neural network models specially optimized for resource-constrained edge devices using model compression and quantization techniques.
Intelligent data filtering
Smart data preprocessing and filtering at the edge to drastically reduce bandwidth requirements and cloud computing costs while maintaining insights.
Privacy-preserving inference
Keep sensitive data local with on-device processing that eliminates the need to send raw data to the cloud, enhancing privacy and security compliance.
Predictive maintenance
Deploy condition monitoring and anomaly detection systems that predict failures before they occur, minimizing downtime and maintenance costs.
Hybrid edge-cloud architecture
Inference on the device for real-time response and privacy; the cloud for fleet analytics, model retraining and updates. We decide the split with you, by latency, bandwidth and cost.
Capabilities and deliverables
Key areas of focus
- Edge device hardware optimization for neural networks
- AI model compression and quantization for embedded systems
- Edge-optimized data preprocessing algorithms
- Secure on-device inference implementation
Deliverables
- Real-time AI processing with LiteRT (TensorFlow Lite) or ONNX Runtime
- Optimized hardware configuration for neural network acceleration
- Efficient edge-cloud data orchestration
- Custom TinyML solutions for ultra-constrained devices
Process
How we work
Feasibility
Establish whether the task fits the target device, using representative data and agreed error limits.
Deliverable: Evaluation dataset and false-positive / false-negative limits
Model on target
Compare the original and converted models; measure latency, RAM, flash and power on the intended device.
Deliverable: Measured results on the deployment hardware
Integration
Budget sensor buffers, preprocessing, model and application; measure the full path from capture to decision.
Deliverable: Integrated pipeline on the target
Field acceptance and handoff
Test representative conditions, document failure cases and plan how a model version is approved and rolled back.
Deliverable: Versioned preprocessing, model and runtime delivered together
Engineering notes
How to evaluate an edge AI or TinyML project
Edge AI development puts model inference on a device or gateway. Feasibility depends on the task, representative data, target hardware and acceptable errors. Evaluate the complete sensing and decision pipeline rather than choosing a processor from its advertised AI throughput alone.
Measure quantization on the target device
Quantization can reduce model size and change inference performance, but accuracy and speed depend on the model, runtime and hardware. Compare the original and converted models using representative evaluation data. Report latency, RAM, flash and power on the intended device rather than promising a universal percentage improvement.
TensorFlow: post-training quantizationTinyML and sensor fusion need a system budget
Budget memory for sensor buffers, preprocessing, model weights, working tensors and the application. Measure the full path from data capture to an output decision. For sensor fusion, define synchronization, calibration and behavior when one sensor is unavailable; inference-only timing misses these dependencies.
Separate model development from field acceptance
Agree the intended operating conditions and acceptable false positives and false negatives before training. Keep evaluation data separate from training data, test representative conditions and document failure cases. Plan how a model version is approved, deployed, monitored and rolled back; automatic retraining is a separate capability.
Key considerations
- Representative data and evaluation criteria agreed first
- Latency and memory measured on the deployment hardware
- False-positive and false-negative limits recorded by use case
- Versioned preprocessing, model and runtime delivered together
Planning
Scope and cost
Start with a non-confidential summary of your product and the milestone you need to reach next.
What to include in an edge AI development brief
Describe the decision the device must make, the available data and the acceptable error rate. An early feasibility study should establish whether the task fits the target device before a full product implementation is estimated.
Inputs for your project brief
- Task definition, representative sample data, data-use permissions and known failure conditions.
- Target hardware, sensor interfaces, RAM and flash limits, power budget and required response time.
- Existing model and preprocessing, evaluation method, acceptable errors and model-update requirements.
Choose the scope for your next milestone
- If the processor, sensors or power budget are still open, include embedded hardware development.
- If the model is ready and needs device integration, scope firmware development.
Agree the deliverables and acceptance criteria
Agree the evaluation dataset and false-positive and false-negative limits. Measure complete pipeline latency, memory and power on the target hardware. Include model, preprocessing, runtime versions, reproducible conversion steps and field-update ownership in the handoff. Price data collection and labeling separately where required.
Agree confidentiality arrangements before sharing proprietary design files.
Cost planning
Proof
Related case studies
Edge AI Robotics
Compute, Perception and On-Device Navigation for Warehouse AMRs
E-commerce logistics provider
- Robots in Pilot Zone
- 20
- On-Robot NPU (INT8)
- 4 TOPS
- Obstacle-Classification Accuracy
- 98.5%
- Bench-tested
Robotic Prosthetic Arm
EMG-Controlled Bionic Limb with Machine Learning
Prosthetics research institute
- Gesture Accuracy
- 94%
- Bench-tested
- Response Latency
- < 100ms
- Bench-tested
- Recognized Movements
- 16 Gestures
RISC-V AI Accelerator
Architecture, RTL and Verification for an Always-On Edge NPU
Consumer electronics semiconductor company
- NPU Efficiency (INT8)
- 10 TOPS/W
- Design target
- Always-On Power Budget
- < 5 mW
- Design target
- MAC Units in Systolic Array
- 256
mmWave People-Counting Sensor
Privacy-Preserving Radar Occupancy Detection with Edge Processing
Smart-building and retail client, Dubai, UAE
- People-count accuracy (counted vs actual)
- >95%
- Design target
- Privacy-preserving radar sensing
- No camera
- Detection runs on the device
- Edge
Industrial IoT
Predictive Maintenance Sensors and Edge AI for a Steel Plant
Steel manufacturer, India (3 plants)
- Reduction in Unplanned Downtime
- 78%
- Client-reported
- Fault-Classification Accuracy
- 94%
- Measured in pilot
- Sensor Nodes Across 3 Plants
- 500+
Connected Fleet
LTE Cat 1bis Telematics Control Unit and Edge AI for Commercial Vehicles
Logistics and transportation company
- Vehicles in Pilot
- 100
- CAN Bus Decoding
- J1939 / OBD-II
- Drowsiness Detection
- 97%
- Measured in pilot
2 more case studies
Digital Twin
Sensor Units and Edge Gateways for a Smart-Building Platform
Commercial real estate developer
- Custom Multi-Sensor Units
- ESP32-S3
- Legacy Systems Bridged
- BACnet / Modbus
- Local Decision Latency
- < 100 ms
- Design target
AI-Powered Oxygen Therapy
Intelligent FiO2 Titration System for Critical Care
Medical equipment manufacturer, India
- Class B Firmware Design
- IEC 62304
- Oxygen Savings
- 35%
- Design target
- Response Time
- < 30 sec
- Design target
FAQ
Frequently asked questions
We deploy optimized AI models using LiteRT (TensorFlow Lite), LiteRT for Microcontrollers (TensorFlow Lite Micro), ExecuTorch, ONNX Runtime, and vendor-specific runtimes like NVIDIA TensorRT and Qualcomm AI Engine Direct. We work with CNNs, RNNs, LSTM networks, and transformer models. Our model optimization includes quantization (INT8, INT16), pruning, and knowledge distillation to fit resource-constrained devices.
We support various edge AI hardware: ARM Cortex-M with DSP extensions, ARM Cortex-A with NEON, dedicated AI accelerators like Google Edge TPU and Hailo, NVIDIA Jetson series, Intel Core Ultra NPUs, specialized MCUs like STM32 with AI capabilities, and FPGA-based custom accelerators. Platform selection is optimized for your performance, power, and cost requirements.
Model optimization combines quantization (32-bit float to 8-bit integer), pruning of redundant weights, knowledge distillation into smaller student models, efficient architecture choices and hardware-aware compilation. INT8 quantization alone typically cuts model size about 4x; pruning or distillation can reduce it further. Accuracy, speed and memory are measured on your data and your target hardware before we commit to a figure.
Performance varies by model and hardware. As published vendor benchmarks, not our measured results: small image-classification models run at roughly 5-20 FPS on a Cortex-M7 at tens to hundreds of milliwatts, 30-60 FPS on Cortex-A cores with NEON at 0.5-2 W, and 100-400 FPS on accelerators such as the Edge TPU at under 2 W. Keyword spotting and sensor anomaly detection can complete in under 10 ms on an MCU. We measure latency and power for your model on your target hardware.
Yes, we implement on-device learning for model adaptation and personalization. Techniques include transfer learning with frozen base layers, federated learning for privacy-preserving updates, online learning for continuous adaptation, and periodic cloud-based retraining with edge data. This enables models to adapt to local conditions while maintaining privacy and reducing cloud dependency.
Model security includes encrypted model storage, secure enclave execution (TrustZone, TEE), input validation to detect adversarial examples, model watermarking to prevent theft, and runtime integrity verification. For sensitive applications, we add differential privacy in training and adversarial training to improve robustness against attacks.
Edge AI enables real-time analysis of vibration, temperature, acoustic, and current sensor data directly on gateway devices or smart sensors using lightweight anomaly detection and time-series classification models, predicting equipment failures hours or days before they occur without requiring cloud connectivity. We deploy models trained on historical failure mode data, optimized to run inference on Cortex-A class processors or dedicated NPUs.
We optimize computer vision pipelines for embedded platforms by using efficient model architectures like MobileNetV3, EfficientDet-Lite, and YOLO-NAS-S, quantized to INT8 precision and compiled for target-specific accelerators such as Google Coral Edge TPU or NVIDIA Jetson's GPU. The full pipeline includes ISP tuning, frame preprocessing with hardware-accelerated resize, batched inference scheduling, and post-processing to maintain target frame rates within strict thermal and power envelopes.
TinyML refers to running machine learning inference on microcontrollers with as little as 256KB of flash and 64KB of RAM, using frameworks like TensorFlow Lite for Microcontrollers or Edge Impulse to deploy quantized models for tasks such as keyword spotting, gesture recognition, and sensor anomaly detection. We handle the full workflow from data collection and model training through quantization-aware training and operator kernel optimization for the target MCU's DSP instructions.
We implement an end-to-end model lifecycle management pipeline that covers versioned model training with experiment tracking, automated validation against edge-specific performance benchmarks including latency and memory footprint, and OTA model update delivery through a secure firmware update channel. Post-deployment monitoring includes inference confidence telemetry, data drift detection, and automated retraining triggers while respecting privacy constraints.
For real-time object detection, we select hardware based on the accuracy-latency tradeoff, ranging from NVIDIA Jetson Orin Nano for complex multi-class detection at 30+ FPS to Hailo-8 for cost-optimized deployments, paired with optimized inference runtimes like TensorRT or ONNX Runtime. The software stack includes GStreamer-based video pipelines with zero-copy buffer management and an application layer handling tracking, counting, or alerting with sub-100ms end-to-end latency.
GPU accelerators like NVIDIA Jetson offer the highest flexibility and peak throughput for complex models but consume more power (5-30W), making them suitable for plugged-in edge servers and robotics. Dedicated NPUs and TPUs like Google Coral Edge TPU and Hailo-8 deliver superior performance-per-watt (2-10 TOPS/W) but are optimized for specific model architectures, requiring careful model design and compilation for optimal results.
What is edge AI?
Edge AI refers to artificial intelligence algorithms processed locally on edge devices rather than in cloud data centers. Edge AI enables real-time inference, lower latency (often milliseconds, depending on the model and hardware), enhanced privacy (data stays on device), and lower bandwidth costs. Common implementations include TinyML on microcontrollers, neural processing units (NPUs), and optimized models using LiteRT (TensorFlow Lite), ExecuTorch or ONNX Runtime.
Source: Industry definition. Related terms: TinyML, inference, neural network, quantization.
Working with Rapid Circuitry
How much does a project cost, and how is pricing structured?
Each project is quoted as a fixed-scope proposal: a statement of work with milestones, deliverables and pricing, sent within 3–5 business days of the discovery call. The price depends on the requirements, how mature the design is, technical complexity, the number of prototype iterations and the validation scope. Longer engagements can also run as a retainer or a hybrid of both. For budgeting before you contact us, our hardware development cost benchmarks and the PCB design, firmware and IoT cost guides give indicative ranges with their assumptions stated. They are planning ranges, not quotes.
How long does a project take?
It depends on the scope and on what already exists when we start. As planning ranges from our cost benchmarks: a single PCB layout takes 2–6 weeks per board and embedded firmware 4–16 weeks. A connected (IoT) product takes 3–12 months from concept to a production-ready design, and a regulated medical device 12–24 months to a 510(k) submission-ready package; from idea to mass production, a typical connected product takes 9–18 months. The proposal sets out a dated milestone plan, and you receive weekly progress updates during the project.
Who owns the intellectual property?
You do. Our contracts assign the intellectual property in the project work product (schematics, PCB design files, firmware source code and documentation) to you on payment, and we hand over the source files, not only fabrication outputs or compiled binaries. The exact terms are set out in the signed agreement for each engagement.
Do you sign NDAs?
Yes. We sign a mutual NDA before any technical discussion of your project. Send your standard NDA or use ours; we return it signed within one business day.
Can you work with clients in the US, Europe and Japan?
Yes. We are based in Hyderabad, India, and work with product teams in India, the United States, the United Kingdom, Germany, Japan, Australia, the Middle East and other markets. Our office hours are 10:30–19:00 India Standard Time (UTC+5:30), Monday to Saturday, and we schedule design reviews across time zones, typically from US Pacific to Central European time. Japan is 3.5 hours ahead of India, so our first four hours overlap the Japanese afternoon. Prototypes and documentation ship by international courier such as DHL or FedEx. Our hardware outsourcing guide covers what to agree before an international project starts.
Do you hand off to manufacturing, and which contract manufacturers do you work with?
Yes. We prepare the manufacturing release package — fabrication and assembly drawings, the BOM with approved manufacturers, programming files and test specifications — and hand it over to the contract manufacturer (EMS) you choose. If you have not chosen one, we help you evaluate candidates through technical capability assessments and a pilot build. See manufacturing support.
Are you ISO 9001 or ISO 13485 certified?
No. Rapid Circuitry does not hold ISO 9001, ISO 13485 or other certificates. Our processes are aligned with ISO 9001:2015, and we design products to the requirements of the standards that apply to them — for example IEC 60601-1 and IEC 62304 for medical devices (with design documentation structured so your ISO 13485 quality system can absorb it) and IEC 61000 for EMC — and we specify IPC-A-610 workmanship classes for assembly. We prepare the product and its documentation for certification testing at accredited laboratories; the certificate or approval is issued for your product by the laboratory, notified body or regulator.
What do you need from me to start?
A short, non-confidential description of the product and what it must do (sensors, connectivity, on-device processing), the target production volume and unit cost, the markets and regulations it must meet (for example FDA, CE or FCC), your timeline, and what already exists: a concept, a proof of concept, schematics, PCB files or firmware. After the NDA, the next step is a 30-minute discovery call with the engineers who would work on the project, followed by the fixed-scope proposal. Send a project brief to begin.
Explore
Related services
IoT solutions
Connected products from one team: sensor hardware, firmware, BLE, LoRaWAN or cellular connectivity, OTA updates and the cloud backend.
Embedded hardware development
System architecture, MCU/SoC selection, schematic and PCB design, board bring-up, validation and manufacturing handoff.
Robotics
Robot control electronics and software: motor control, ROS 2 integration, sensor fusion and functional-safety design.