AI & Edge Computing

We specialize in embedding advanced artificial intelligence (AI) and machine learning (ML) capabilities directly into edge devices and embedded systems. Our solutions enable real-time data processing and inference at the edge, reducing latency, lowering bandwidth usage, enhancing data privacy, and improving system responsiveness. By integrating AI on constrained hardware, we empower devices to perform complex tasks locally—enabling smarter, faster, and more autonomous operation in diverse applications.

KEY HIGHLIGHTS

TinyML & Embedded AI Model Development

Creation and deployment of lightweight ML models optimized for microcontrollers and resource-constrained devices using frameworks such as TensorFlow Lite for Microcontrollers, Edge Impulse, and CMSIS-NN. We tailor models for classification, regression, anomaly detection, and sensor fusion.

On-Device Inference & Real-Time Processing

Enabling devices to perform inference on sensor data, images, audio, and other inputs without relying on cloud connectivity. This supports use cases requiring immediate decisions, offline operation, or privacy-sensitive processing.

End-to-End AI Workflow

Comprehensive services covering data collection, labeling, model training and validation, quantization, pruning, and deployment. We integrate continuous learning pipelines and firmware update frameworks to keep AI models current and effective.

Edge AI Platforms & Hardware Integration

Expertise in platforms including ARM Cortex-M MCUs, ESP32, NVIDIA Jetson Nano, Google Edge TPU, Rockchip RK3588, and Raspberry Pi. We optimize performance and power consumption tailored to target hardware.

Custom Algorithm Design

Development of specialized algorithms for gesture recognition, predictive maintenance, anomaly detection, image and audio classification, sensor fusion, and contextual awareness—enhancing device autonomy and intelligence.

Model Compression & Optimization

Application of quantization, pruning, knowledge distillation, and hardware acceleration to reduce model size and inference latency while maintaining accuracy on constrained devices.

Secure & Scalable Model Deployment

Implementation of OTA update mechanisms for AI models and firmware, ensuring secure, efficient, and scalable model refresh across distributed device fleets.

AI & EDGE COMPUTING CAPABILITIES

Embedded AI and TinyML model deployment on MCUs, SoCs, and edge processors

Real-time on-device inference for vision, audio, and sensor data applications

Full AI workflow: data collection, training, quantization, and edge deployment

Model compression and optimization for low-latency, low-power execution

Integration with TensorFlow Lite, Edge Impulse, PyTorch, and CMSIS-NN frameworks

Edge platform expertise: NVIDIA Jetson, Google Edge TPU, ESP32, and Raspberry Pi

Secure OTA updates and lifecycle management for AI models and firmware

Applications in predictive maintenance, gesture recognition, wearables, and robotics

USE CASES

  • •Predictive Maintenance: Edge AI monitors equipment for early fault detection and reduces downtime.
  • •Smart Vision Systems: Real-time object, gesture, and quality inspection on embedded devices.
  • •Autonomous Robotics & Drones: Edge processing for navigation, obstacle avoidance, and sensor fusion.
  • •Smart Environment Monitoring: Detects air quality, motion, and environmental anomalies for automation.
  • •Audio & Voice Processing: Local wake-word detection, noise classification, and environmental sound recognition.
  • •Healthcare & Wearables: On-device analysis of heart rate, motion, and vitals for personalized health alerts.

Client Success

Don't Take Our Word For It, Read Our Client Success Stories

HR

ZyntelliTech exceeded our expectations in delivering a custom firmware solution for our IoT distance measurement device. Their team expertly developed firmware on the Nordic nRF9160 platform, integrating a Time-of-Flight (ToF) sensor to measure distance with high precision and consistency. In addition to accurate sensor integration, ZyntelliTech implemented GPS functionality and designed a reliable data pipeline to transmit real-time sensor readings and location data to our AWS cloud infrastructure. Their deep understanding of LTE-M/NB-IoT communication, MQTT protocols, and cloud connectivity enabled seamless, low-latency performance across our system. Throughout the project, ZyntelliTech demonstrated excellent technical skills, clear communication, and a proactive approach to problem-solving. Their firmware was well-structured, power-optimized, and thoroughly documented—making it easy to scale and maintain. We highly recommend them as a development partner for any IoT, cloud-connected, or sensor-driven project.

Hunter Raley, CEO

CR

We partnered with ZyntelliTech Development LLC to develop a custom IoT solution based on the ESP32-S3 platform—and the results were exceptional. Their team engineered a reliable, low-power controller that captures real-time UV sensor data and seamlessly transmits it to our AWS cloud infrastructure. ZyntelliTech handled every layer of the system: from embedded firmware with OTA update capabilities and AWS IoT Core fleet provisioning, to full mobile app development using Flutter. The mobile apps allow users to monitor and manage devices effortlessly, with real-time data visualization and secure device onboarding. Their ability to deliver a tightly integrated solution—from hardware to cloud to mobile—was exactly what we needed. The firmware was efficient, the cloud communication was rock-solid, and the apps were beautifully designed and intuitive. ZyntelliTech proved to be a reliable and forward-thinking development partner, and we highly recommend them for any end-to-end IoT project.

Charles Rigby, CEO

DC

ZyntelliTech Development LLC delivered a complete RF control solution for our vehicle system, handling both hardware and firmware development with precision and professionalism. Their team designed custom electronic boards for the RF transmitter and receiver modules, ensuring reliable long-range communication and signal integrity under demanding conditions. In addition to hardware design, ZyntelliTech developed embedded firmware optimized for responsive control, robust pairing, and interference resistance. The system performed flawlessly in field tests, demonstrating the team's deep understanding of RF communication protocols and automotive-grade reliability. This project showcased ZyntelliTech’s end-to-end capability—from schematic and PCB design to low-level embedded programming and RF performance tuning. We highly recommend them for any project requiring custom wireless control systems or vehicle integration.

Dennis Chim, Co-Founder

AN

ZyntelliTech Development LLC played a pivotal role in the successful development of our advanced wireless sensor system. The team engineered a scalable solution using Nordic’s nRF52 series for BLE mesh networking and the nRF9160 as a cellular gateway, seamlessly connecting edge devices to our AWS cloud infrastructure. ZyntelliTech provided full-stack services—including custom PCB design, low-power firmware development, and end-to-end AWS integration. Their implementation of a robust BLE mesh network enabled reliable, long-range communication between sensor nodes, while the nRF9160 gateway ensured real-time data transmission over LTE-M with secure cloud connectivity. In addition to the technical architecture, they managed prototype manufacturing and hardware validation, delivering a complete, production-ready system. Their deep expertise in embedded systems, wireless communication, and cloud-connected IoT made them an invaluable partner. We highly recommend ZyntelliTech for complex IoT product development and deployment.

Alexander Nunez, Founder