Technical Solutions

Six engineering domains at the hardware-software boundary. Principal-level practical experience, strict quality and confidentiality standards.

Computer Vision & Spatial Computing

Machine vision pipelines, real-time image processing, optical tracking, and SLAM for automotive, robotics, and industrial applications.

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Real-Time Image Processing

Sub-millisecond pipelines for industrial inspection, defect detection, and quality assurance — running on edge hardware in production environments.

Visual SLAM & Spatial Mapping

Stereo vision, LiDAR fusion, and visual-inertial odometry for autonomous navigation and 3D reconstruction.

Multi-Camera Tracking

Object tracking across overlapping and non-overlapping camera arrays with identity persistence and trajectory prediction.

Calibration & Sensor Fusion

Intrinsic and extrinsic camera calibration, multi-sensor alignment, and temporal synchronization frameworks.

// Multi-camera stereo pipeline cv::Mat disparity; auto sgbm = cv::StereoSGBM::create( 0, 128, 5, 8 * 5 * 5, // P1 32 * 5 * 5 // P2 ); sgbm->compute(left, right, disparity); // Reproject to 3D point cloud cv::Mat points3D; cv::reprojectImageTo3D(disparity, points3D, Q);

Technology Stack

OpenCV 4.xCUDATensorRTV4L2GStreamerRealSense SDKORB-SLAM3Open3DHalcon (MVTec)C++17/20PythonNVIDIA Jetson

Applied AI & Edge Computing

CNN deployment, model optimization, TensorRT inference, and NVIDIA Jetson edge computing for real-time applications.

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Model Optimization & Quantization

INT8/FP16 quantization, layer fusion, and TensorRT engine building for 10-50x inference speedups without significant accuracy loss.

NVIDIA Jetson Deployment

Full-stack deployment on Jetson Nano, Xavier NX, and AGX Orin — from JetPack SDK configuration to custom L4T images.

Custom CNN Architectures

Task-specific network design. Detection, segmentation, classification, and anomaly detection models tuned for your data and latency budget.

Edge-Cloud Pipelines

Hybrid architectures where edge devices handle real-time inference while cloud infrastructure manages training, retraining, and fleet management.

// TensorRT engine builder auto builder = nvinfer1::createInferBuilder(logger); auto config = builder->createBuilderConfig(); config->setFlag(BuilderFlag::kFP16); config->setFlag(BuilderFlag::kINT8); config->setInt8Calibrator(calibrator.get()); auto engine = builder->buildSerializedNetwork( *network, *config ); // INT8: ~4x throughput vs FP32

Technology Stack

TensorRTONNX RuntimeCUDA 12cuDNNPyTorchJetPack SDKDeepStreamTriton InferenceNVIDIA TAOOpenVINOC++17/20Python

Embedded Systems, Automotive & Robotics

Yocto builds, STM32 firmware, AUTOSAR, FreeRTOS, ROS2 integration, and embedded Linux for safety-critical and real-time applications.

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Embedded Linux / Yocto BSPs

Custom board support packages, kernel configuration, device tree overlays, and production-grade Linux distributions for embedded platforms.

STM32 & ARM Cortex Firmware

Bare-metal and RTOS-based firmware for STM32, NXP i.MX, and custom SoCs. HAL-level peripheral drivers, bootloaders, and OTA update systems.

AUTOSAR & Functional Safety

Classic and Adaptive AUTOSAR integration, ISO 26262 design considerations, and automotive-grade software processes.

ROS2 & Robotics Middleware

Node architecture, DDS configuration, real-time scheduling, navigation stacks, and sensor integration for robotic systems.

// FreeRTOS real-time control loop void vControlTask(void* params) { TickType_t xLastWake = xTaskGetTickCount(); for(;;) { sensor_read(&imu_data); pid_update(&controller, imu_data); actuator_write(controller.output); vTaskDelayUntil(&xLastWake, pdMS_TO_TICKS(1)); // 1kHz loop } }

Technology Stack

Yocto / OpenEmbeddedSTM32 / ARM CortexFreeRTOSZephyrROS2 HumbleAUTOSARCAN / CANopenEtherCATDevice TreeU-BootC / C++Rust (embedded)

High-Performance Systems (C/C++)

Low-latency architecture, SIMD/AVX optimization, CUDA GPU kernels, and algorithm design for performance-critical applications.

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SIMD/AVX Vectorization

Manual and compiler-guided vectorization for image processing, signal processing, and numerical computation. SSE4, AVX2, AVX-512, and NEON.

CUDA GPU Kernels

Custom CUDA kernels for parallel computation — image processing, matrix operations, physics simulation, and ML inference acceleration.

Low-Latency Architecture

Lock-free data structures, memory-mapped I/O, kernel bypass networking, and CPU affinity tuning for sub-microsecond latency paths.

Algorithm Optimization

Cache-oblivious algorithms, SoA/AoS layout optimization, branch prediction tuning, and profiler-guided hotpath engineering.

// AVX2 vectorized dot product float dot_avx2(const float* a, const float* b, size_t n) { __m256 sum = _mm256_setzero_ps(); for (size_t i = 0; i < n; i += 8) { __m256 va = _mm256_load_ps(a + i); __m256 vb = _mm256_load_ps(b + i); sum = _mm256_fmadd_ps(va, vb, sum); } // horizontal sum float result[8]; _mm256_store_ps(result, sum); return result[0] + result[1] + ...; }

Technology Stack

C++17/20/23CUDA 12AVX2 / AVX-512ARM NEONIntel TBBOpenMPVulkan Computeperf / VTuneValgrind / ASanCMake / ConanGCC / ClangLLVM/MCA

Enterprise Software (C#/.NET)

Scalable ASP.NET backends, high-throughput corporate tooling, and Windows ecosystem integration.

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ASP.NET Core Services

RESTful and gRPC APIs, microservice architectures, background processing, and event-driven systems with guaranteed delivery.

High-Throughput Data Pipelines

ETL systems, data warehousing, real-time analytics, and integration with existing enterprise data landscapes (SQL Server, Oracle, SAP).

Corporate Tooling

Internal platforms, workflow automation, reporting dashboards, and custom LOB applications that replace spreadsheet-driven processes.

Windows Ecosystem Integration

Active Directory, Azure AD, Windows Services, MSMQ, COM interop, and legacy system modernization paths.

// High-throughput event processor public class EventProcessor : BackgroundService { protected override async Task ExecuteAsync( CancellationToken ct) { await foreach (var batch in _channel.Reader.ReadAllAsync(ct)) { await ProcessBatch(batch); _metrics.Processed(batch.Count); } } } // 50K+ events/sec on single node

Technology Stack

.NET 8ASP.NET CoreEntity FrameworkgRPCMassTransitSQL ServerPostgreSQLRedisRabbitMQAzureDocker / K8sBlazor

Simulation, Digital Twins & Interactive Systems

Physics simulations, synthetic training data, digital twin environments, and interactive learning platforms.

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Physics Simulation

Real-time and offline physics engines for vehicle dynamics, fluid simulation, structural analysis, and multi-body systems.

Synthetic Training Data

Procedurally generated datasets with pixel-perfect annotations — domain randomization for CNN training when real data is scarce or expensive.

Digital Twin Environments

Virtual replicas of physical systems for monitoring, prediction, and what-if analysis. Connected to real-time sensor feeds via MQTT/OPC-UA.

Interactive Training Platforms

LMS-backed learning environments with immersive 3D content, assessment engines, and SCORM/xAPI compliance for enterprise training.

// Synthetic dataset generator for scene in domain_randomizer.generate(): rgb = renderer.render(scene, camera) depth = renderer.render_depth(scene, camera) masks = renderer.render_segmentation(scene) annotations = { "bbox": extract_bboxes(masks), "segmentation": encode_rle(masks), "depth_map": depth.astype(np.float16) } dataset.save(rgb, annotations) # 100K annotated images / hour

Technology Stack

Unreal Engine 5UnityNVIDIA OmniverseIsaac SimCARLABlender (scripted)PhysXVulkan / DirectX 12OPC-UA / MQTTSCORM / xAPIC++ / PythonWebGL / Three.js

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Our practical experience spans the full hardware-software stack. Let's discuss your specific challenge.

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