We partner with startups and enterprises to develop, optimize, and ship production-grade computer vision and video analytics solutions.
Cutting-edge computer vision is often too expensive to deploy — prohibitive hardware and compute costs kill projects before they launch. We change that through algorithm optimization, model compression, and efficient pipeline design, making advanced CV viable on affordable hardware.
Four focused programs to move from idea to production — each starts with a free, no-obligation call.
End-to-end computer vision and video analytics engineering — from model selection and optimization to production pipelines that run cost-effectively on the hardware you already have.
The software team that takes you from carrier board to fleet — with NVIDIA Jetson Orin systems already in production from Nano to AGX across multiple continents.
Prove your hardest computer vision problem is solvable — in two weeks, on your own data. A working demo, an honest go/no-go with the numbers, and a costed path to production.
Your PoC works — we turn it into a production, glass-to-glass system: live cameras in, optimized inference on edge and cloud GPUs, results out. Then we hand it over so your team can run it.
We help customers choose, train, modify, validate, and optimize deep neural networks to achieve top-level quality and performance.
Our training approach goes beyond the conventional methods that rely on pre-arranged datasets. We incorporate advanced techniques, including human-guided training, the use of synthetic data, enhanced augmentation processes, and strategic heuristics. We meticulously craft our training strategy to ensure more efficient models achieve the targeted quality and processing speed.
We assist our clients in reducing their resource requirements by refining models through quantization, structural pruning, and modifications in model design. Our methods extend to the comparative analysis of various models, aiming to identify the most resource-efficient option within a model family or across diverse models. Additionally, we specialize in optimizing models for TensorRT, which stands as the premier inference environment for CUDA runtime.
Model validation is an essential phase in the training of neural networks. It guarantees that the model excels with the training data and maintains its performance with new, unexposed data. This process entails establishing metrics that are significant from both a data science perspective and in terms of business process requirements, particularly in real-world applications. Our approach to validation involves designing experiments that test individual models and entire pipelines.
We help build high-performance computer vision and video analytics pipelines and distributed systems, including edge and data center components.
As specialists in the development of computer vision frameworks and custom contracting, we have an extensive knowledge of computer vision architecture and its real-world applications. Our expertise encompasses leading-edge tools including DeepStream, TensorRT, OpenCV CUDA, CuPy, and Nvidia hardware platforms. Leveraging this knowledge, we have created Savant, a distinctive framework that embodies the optimal architecture for computer vision applications. Our approach is designed to assist our clients in crafting cost-effective solutions, accelerating the development process while minimizing the need for intricate low-level coding.
Whether your preference lies with PyTorch, DeepStream, bare TensorRT, or a solution based on Savant, our team is fully equipped to assist. We specialize in developing pipelines from the ground up or transforming your existing pipelines into more optimized frameworks. Our expertise includes refining models, heuristics, and pipeline code using suitable tools and low-level code in C/C++, Rust, and CUDA kernels. Our hardware capabilities encompass the Nvidia Jetson series and data center GPUs. We have dedicated thousands of hours to meticulously crafting and harmonizing code to ensure its optimal performance on these platforms, with a strong emphasis on bug-free operation.
The evolving landscape of greener computing and worldwide connectivity presents fresh challenges for computer vision and IoT applications. From transport solutions to autonomous agents, and from mobile applications to conventional video analytics, businesses now have access to a wide array of hardware. This hardware facilitates the optimization of energy consumption, reliability, and performance by balancing computational tasks between edge devices and data centers. A well-conceived hybrid strategy enhances the operational efficiency, cost-effectiveness, and manageability of contemporary computer vision applications.
We empower customers to reduce expenses, mitigate risks, and acquire crucial knowledge in developing computer vision and video analytics.
Savant is intricately built upon DeepStream, positioning us as leading experts in this domain. Our team has dedicated thousands of hours to developing and fine-tuning DeepStream, gaining in-depth knowledge of its nuances, including challenges and limitations that are not commonly addressed in official documentation or Nvidia developer forums. This expertise enables us to reduce development costs and significantly mitigate risks for our clients.
Leveraging our expertise in DeepStream, we are constantly utilizing TensorRT in our operations. This extensive experience equips us to guide our clients effectively through the process of adapting their models for TensorRT. Our support encompasses a range of applications, including deployment for DeepStream, Triton, Savant, and standard TensorRT, as well as quantization and performance evaluation, ensuring a comprehensive and professional service.
As the creators of this technology, we assist our clients in thoroughly exploring the framework, gaining a deep understanding of its design and architecture. Our expertise is vital in crafting solutions for edge, data center, or hybrid pipelines, especially those that require integration with third-party systems and are designed for scalable, high-load processing. We also provide guidance in developing a strategy and plan for deploying in production environments, whether it involves using Docker Compose, Kubernetes (K8s), or other methods without additional orchestration layers.
Contact us to discuss how we can help with your computer vision project.
Get in Touch