Frequently Asked Questions

We work with Raspberry Pi 4/5, Jetson Nano, Banana Pi M5, STM32, ESP32, and custom boards. With Yocto, we can target most ARM/x86 platforms with a supported BSP.

Pre-built distributions include thousands of packages you do not need, bloating your rootfs and attack surface. Yocto builds exactly what you specify - minimal footprint, predictable boot time, and full control over every component. For production embedded hardware, this difference is critical.

Most common: CcspWifi for Wi-Fi management stack integration, CcspTr069 for ACS connectivity, custom CcspComponents for proprietary feature exposure through TR-181, and parodus2ccsp for WebPA cloud service integration.

Fine-tuning retrains the model on your data - expensive, slow to update, and still prone to hallucination. RAG retrieves relevant documents at query time, grounding every response in your actual data. For domain-specific applications, RAG is almost always the better choice: cheaper, faster to update, and fully auditable.

Gazebo (Ignition Gazebo for ROS 2 Humble+). All robots are described in URDF and fully tested in simulation before physical deployment. We run navigation and perception tests headlessly in Gazebo on every CI push.

Pre-built distributions include thousands of packages you do not need, bloating your rootfs and attack surface. Yocto builds exactly what you specify - minimal footprint, predictable boot time, and full control over every component. For production embedded hardware, this difference is critical.

Most common: CcspWifi for Wi-Fi management stack integration, CcspTr069 for ACS connectivity, custom CcspComponents for proprietary feature exposure through TR-181, and parodus2ccsp for WebPA cloud service integration.

Fine-tuning retrains the model on your data - expensive, slow to update, and still prone to hallucination. RAG retrieves relevant documents at query time, grounding every response in your actual data. For domain-specific applications, RAG is almost always the better choice: cheaper, faster to update, and fully auditable.

Gazebo (Ignition Gazebo for ROS 2 Humble+). All robots are described in URDF and fully tested in simulation before physical deployment. We run navigation and perception tests headlessly in Gazebo on every CI push.

Yes. All proprietary code we write is fully owned by you. We provide complete handover and documentation at project completion.

We write kernel drivers as loadable kernel modules following the standard Linux driver model, targeting the relevant subsystem (IIO, V4L2, HID, SPI, etc.). We always prefer upstream drivers over out-of-tree forks to reduce long-term maintenance burden.

Raspberry Pi 4/5, Banana Pi M5, Jetson Nano, and custom BCM-based reference platforms. We can port the RDK-B stack to new hardware given a working Yocto BSP layer for the target platform.

We use Isolation Forest for multivariate anomaly detection across CPU, memory, disk I/O, and network metrics - trained on 30 days of historical telemetry. It alerts on pattern deviations from the learned baseline, not on absolute thresholds. This catches slow degradation that simple threshold alerts miss entirely.

Yes, with caveats. We develop AI and perception components for medical robotics systems, and integrate lightweight models for real-time inference on collected real-world data within robotic control loops.

We write kernel drivers as loadable kernel modules following the standard Linux driver model, targeting the relevant subsystem (IIO, V4L2, HID, SPI, etc.). We always prefer upstream drivers over out-of-tree forks to reduce long-term maintenance burden.

Raspberry Pi 4/5, Banana Pi M5, Jetson Nano, and custom BCM-based reference platforms. We can port the RDK-B stack to new hardware given a working Yocto BSP layer for the target platform.

We use Isolation Forest for multivariate anomaly detection across CPU, memory, disk I/O, and network metrics - trained on 30 days of historical telemetry. It alerts on pattern deviations from the learned baseline, not on absolute thresholds. This catches slow degradation that simple threshold alerts miss entirely.

Yes, with caveats. We develop AI and perception components for medical robotics systems, and integrate lightweight models for real-time inference on collected real-world data within robotic control loops.

Timelines depend on scope and hardware constraints. We provide detailed estimates during discovery with clear milestones and deliverables.

Layered approach: unique per-device credentials at manufacturing, Secure Boot with U-Boot Verified Boot, read-only rootfs with writable overlay, systemd service hardening, attack surface reduction, and automated CVE monitoring in CI.

Yes. We integrate Camera Modules via V4L2 and MIPI CSI-2, and support various display interfaces including HDMI, DSI, and SPI-connected TFT panels. We also integrate IoT devices via Zigbee, Z-Wave, and BLE using standard gateway middleware.

TensorFlow Lite Micro on ESP32 with 520KB SRAM, and YOLO v8n at 40 FPS on Raspberry Pi 5 with ONNX Runtime. The right target depends on latency, power, and accuracy requirements.

Vision: Intel RealSense D435i, Arducam MIPI CSI-2, USB3 cameras. Ranging: 2D/3D LiDAR (SICK, RPLiDAR). Motion: IMU (ICM-42688, BMI270). All through ROS 2 standardised interfaces.

Layered approach: unique per-device credentials at manufacturing, Secure Boot with U-Boot Verified Boot, read-only rootfs with writable overlay, systemd service hardening, attack surface reduction, and automated CVE monitoring in CI.

Yes. We integrate Camera Modules via V4L2 and MIPI CSI-2, and support various display interfaces including HDMI, DSI, and SPI-connected TFT panels. We also integrate IoT devices via Zigbee, Z-Wave, and BLE using standard gateway middleware.

TensorFlow Lite Micro on ESP32 with 520KB SRAM, and YOLO v8n at 40 FPS on Raspberry Pi 5 with ONNX Runtime. The right target depends on latency, power, and accuracy requirements.

Vision: Intel RealSense D435i, Arducam MIPI CSI-2, USB3 cameras. Ranging: 2D/3D LiDAR (SICK, RPLiDAR). Motion: IMU (ICM-42688, BMI270). All through ROS 2 standardised interfaces.

Yes. We follow your conventions and CI/CD, and work through code review to integrate smoothly without disrupting your team.

Yes. PREEMPT-RT kernel patch achieves 50-100 us worst-case interrupt latency on Cortex-A hardware with CPU isolation and mlockall(). For tasks requiring under 10 us, we use a co-processor (FreeRTOS on M4 core) alongside the main Linux processor.

Yes. We are familiar with the RDK certification test suite and can identify gaps, write custom CcspComponents to pass specific test cases, and resolve TR-181 data model compliance issues.

DVC for dataset versioning, MLflow for experiment tracking, Great Expectations for data validation, Docker for reproducible training, GitHub Actions for CI, and a canary deployment strategy for model promotion to production.

We calibrate simulation parameters against physical measurements: drive a known distance and tune odometry, measure sensor noise floor and replicate in simulation. Target: under 10% discrepancy before design commitment.

Yes. PREEMPT-RT kernel patch achieves 50-100 us worst-case interrupt latency on Cortex-A hardware with CPU isolation and mlockall(). For tasks requiring under 10 us, we use a co-processor (FreeRTOS on M4 core) alongside the main Linux processor.

Yes. We are familiar with the RDK certification test suite and can identify gaps, write custom CcspComponents to pass specific test cases, and resolve TR-181 data model compliance issues.

DVC for dataset versioning, MLflow for experiment tracking, Great Expectations for data validation, Docker for reproducible training, GitHub Actions for CI, and a canary deployment strategy for model promotion to production.

We calibrate simulation parameters against physical measurements: drive a known distance and tune odometry, measure sensor noise floor and replicate in simulation. Target: under 10% discrepancy before design commitment.

Yes. We routinely work under mutual NDAs and treat firmware and AI IP as highly sensitive.