Edge AI & Machine Learning Deployment

Our Edge AI Deployment service is the end-to-end process of taking your trained model and deploying it onto constrained embedded hardware. We are not a data-science-for-hire firm; we are expert Embedded AI Engineers. Our work begins after your data scientists have a trained model. We dive into the deep technical work of model analysis, conversion, quantization, and optimization, porting it to run with maximum performance on your specific target hardware. This solves the critical business problem of a model that is "too big, too slow, and too power-hungry."

For example, a medical device client came to us with a powerful audio model for real-time cough detection that was over 500MB. We applied advanced 8-bit quantization and pruning, reducing the model size by 90% with less than 1% accuracy loss, enabling it to run on their low-cost, low-power STM32MP1-based device.

We are experts in all forms of on-device AI, from high-speed computer vision to complex multi-sensor fusion (e.g., combining accelerometer and gyro data), direct analog sensor inference (like vibration analysis), and digital signal processing (DSP)-based pattern detection. 

• Generative AI (The Creative Partner): Our GenAI partner generates the critical "glue logic" and infrastructure. Trained on our best-practice Yocto builds, it auto-generates the bitbake recipes to compile complex AI libraries (like OpenCV or GStreamer) and drafts the C++ inference engine code and video pipelines, all based on our proven, stable architectures.
• Machine Learning (The Analytical Partner): This is our key differentiator. When you provide a model and a list of potential hardware, our ML partner predicts its real-world performance. For a smart-city client, our Co-Pilot analyzed their YOLOv7 people-counting model and predicted it would only achieve 4 FPS on their chosen MPU. It recommended a Google Coral module instead, which our analysis correctly predicted would achieve 70+ FPS for a similar cost, saving them a 3-month hardware re-spin and over $50k in NRE costs. This is not just deployment; it's a data-driven architecture consultation that saves you months of guesswork.

Our metrics are our proof: we have successfully deployed over 30+ unique AI/ML models onto embedded hardware, turning our clients' data-science R&D into shippable products

Case Study 1: The "Too-Slow" Industrial Vision System

Problem: A client had a brilliant Keras/Python model for detecting manufacturing defects on an assembly line. It worked perfectly on a developer's laptop (achieving 30 FPS) but was unusable on their embedded prototype board (an NXP i.MX 8M Plus), running at only 2 FPS.

Process: We didn't retrain the model. We optimized it. Our team profiled the model, identified the bottlenecks, and then converted it using TensorFlow Lite with full INT8 quantization. We then used the NXP eIQ toolkit to write a C++ application that deployed the model to run directly on the i.MX 8's dedicated NPU, completely bypassing the main ARM cores. We also built a GStreamer pipeline to create a zero-copy data path from the MIPI camera directly to the NPU.

Result: We delivered a final, on-device application that achieved 28 FPS, a 14x performance increase, while fitting the model in 25% of the original memory footprint. This saved the client from a costly hardware redesign and allowed them to ship their product.

We engage with clients at any stage, providing precisely the value they need.

As a Standalone Service (Model Deployment): You have a trained model (e.g., in .h5, .pth, or ONNX format) and your target hardware. Our team will perform the deep optimization, quantization, conversion, and deployment to get your model running at maximum performance on your existing platform.

As an Integrated End-to-End Solution (The "AI-First" Hardware): This is our most powerful offering. You have an AI goal, but no hardware. For example, a retail-tech client wanted a smart kiosk that responded to hand gestures. We engaged for the full Custom Embedded Linux Development and Edge AI Deployment service. We used our AI Co-Pilot to select a Rockchip RK3568 MPU, then built a custom Yocto OS, a V4L2 camera pipeline, and deployed a lightweight gesture model to its NPU. The result was a single, cost-effective board that ran a 4K UI on its GPU, while simultaneously running the AI gesture model on its NPU with a <100ms response time. The hardware, the custom OS, the drivers, and the AI libraries are all co-designed and delivered as a single, fully-validated, production-ready system.

This is a critical strategic decision. Your primary alternatives are the cloud or a difficult DIY approach.

The Generic/Vendor Trap (The "Cloud AI" Trap): The "easy" path is to send all your data (video, audio, etc.) to a cloud API (like AWS or Azure AI). This is a trap that creates a competitively weak product. It's expensive (you pay for every inference), slow (high latency), unreliable (what happens if the internet connection drops?), and a massive privacy and security risk (you are sending raw user/factory data to a third party).

The In-House Labyrinth (The "Data Scientist vs. Embedded" Trap): This is the #1 reason AI projects fail. Your data scientists are brilliant, but they live in Python, Keras, and Jupyter notebooks. Your embedded engineers are brilliant, but they live in C, Yocto, and hardware drivers. They don't speak the same language. Your team will spend 6-9 months just trying to compile TensorFlow Lite with the correct hardware acceleration, all while debugging cryptic driver and dependency errors.
The Expert Partner Solution: We are the translators. We are the "Embedded AI Engineers" who live in both worlds. We take the model file from your data science team and deliver a clean, simple, high-performance API (run_inference()) to your embedded application team. We handle the entire complex "middle layer," allowing your teams to do what they do best.

• Phase 2 (Commercials): Formal Deployment & Optimization Plan. We provide a detailed proposal outlining the conversion path, the optimization strategy (e.g., quantization, pruning), the target hardware, and a firm timeline and quote.
• Phase 3 (Execution): Model Optimization & Conversion. This is the deep-tech work. Our team converts your model to an inference-ready format (like .tflite or ONNX) and applies advanced techniques like INT8 or FP16 quantization to make it smaller and faster.
  Phase 4 (Execution): Inference Engine & Pipeline Integration. We build the C/C++ application for your Custom Embedded Linux Development system. This includes integrating the vendor's SDK (e.g., NVIDIA TensorRT, Rockchip RKNN) and building the high-performance data pipelines (e.g., GStreamer, V4L2) to feed data from your camera or sensors to the model.
  Phase 5 (Handoff & Support): Final Model & Benchmark Report. We deliver the final, optimized model, the inference engine binaries, and the SDK. Most importantly, we deliver a comprehensive benchmark report that proves the final on-device FPS, RAM usage, and CPU/NPU load.

Do you train AI models, or just deploy them?
We are deployment and optimization experts. We expect you to bring your own trained model. However, we often partner with your data science team to advise them on which model architectures are "hardware-friendly." For an industrial IoT client, we advised them against a complex neural network for predictive maintenance. We recommended a simple Random Forest model, which we then deployed using TensorFlow Lite for Microcontrollers on an STM32MP1. The final model used <1MB of RAM and achieved their 98% accuracy target, saving them from a costly and unnecessary hardware upgrade.

What's the difference between CPU, GPU, NPU, and TPU? 
In short:CPU: Slowest, most generic. Bad for most AI.
GPU: Good at parallel math, used by NVIDIA Jetson for complex models.
NPU (Neural Processing Unit): A dedicated, on-chip AI accelerator. This is the key to efficient, low-power AI on NXP, Rockchip, and ST MPUs.
TPU (Tensor Processing Unit): Google's custom-built AI accelerator, found on Google Coral modules. Our job is to ensure your model runs on the correct accelerator, not just the CPU.
My project doesn't use a camera. Can you run AI on other sensors?
Absolutely. This is a core specialty and a major trend. Many of our most innovative projects do not involve video. We are experts in:

• Analog Signal Inference: Capturing high-frequency data from analog sensors (like vibration, current, or audio) and running AI models (like 1D-CNNs or LSTMs) to detect anomalies. This is the foundation of modern predictive maintenance.
• Multi-Sensor Fusion: We build systems that fuse data from multiple sensors (e.g., accelerometer, gyroscope, and magnetometer) to create a complete picture of a device's state.
• Innovative Sensor Use: We've deployed models for advanced pattern detection from simple sensors. A great example is using an accelerometer in a wearable, not just for "step counting," but to run a complex model that can classify sleep patterns or detect a fall. 

What is "quantization"? Will it hurt my model's accuracy? Quantization is the process of converting a model's math from high-precision 32-bit floating point (FP32) to low-precision 8-bit integer (INT8). This makes the model ~4x smaller and ~4x-10x faster. While there can be a tiny (0.5-2%) accuracy loss, we use advanced techniques (like post-training quantization) to minimize this, giving you a massive performance boost for a negligible trade-off.

How do you handle real-time video feeds for AI vision models? : This is one of our specialties. We are experts at building GStreamer and V4L2 pipelines on Linux. We build "zero-copy" pipelines that send the video data directly from the camera's memory to the NPU/GPU's memory, without ever touching (and slowing down) the main CPU. This is essential for achieving 30+ FPS on embedded hardware.

What hardware platforms do you specialize in? 
We are platform-agnostic but have deep, production-level expertise with all major AI-enabled toolkits, including:

• NVIDIA Jetson (Nano, Orin) using TensorRT
• NXP i.MX 8 & 9 (8M Plus, 93) using eIQ Toolkit
• Google Coral (Mini PCIe, M.2) using the Edge TPU runtime
• Rockchip (RK3568, RK3588) using the RKNN Toolkit
• STMicroelectronics (STM32MP1/2) using STM32Cube.AI
  Intel (Atom, Core) using OpenVINO