AI Computer Vision for Advanced Imaging Systems

Our R&D Partnership Model is designed for deep, effective collaboration. We act as a dedicated technical partner and work closely alongside your engineering team—providing hands-on development, code, and analysis—to solve the specific algorithm challenges that are blocking your progress.

We partner with R&D-intensive companies, from well-funded startups to established technology leaders, who are building products around advanced sensor systems. Our expertise is most valuable in fields like security, medical imaging, industrial non-destructive testing, autonomous systems (RADAR/LiDAR), and geospatial imaging.

It begins with a technical conversation where we listen to your specific problem setup and challenges. This is followed by an analysis of your data, existing models, and technical stack. From there, we deliver a clear assessment of the problems and challenges, along with a hands-on proposal for how we can solve them together. We believe in providing actionable insights from the very first interaction.

While every partnership is tailored to your unique needs, the complex, mission-critical challenges we solve require a deep, hands-on approach. To achieve a genuine breakthrough, a typical engagement runs from 3 to 12 months. This ensures we can move beyond surface-level analysis and deliver the robust, reliable performance your product requires. Many of these partnerships evolve into long-term support throughout the R&D lifecycle.

The primary outcome is the de-risking and acceleration of your product roadmap. By solving your most difficult technical challenges, we provide the confidence your leadership needs to invest, the reliability your product needs to launch, and the speed you need to win in the market.

Our role is to be your partner, and we are flexible in how we contribute. In many cases, our novel work can form the basis of a patent application, and we are experienced in providing the necessary documentation to support your IP filings. If academic publication is a goal for your R&D team, we are also happy to contribute as co-authors.

We specialize in challenging, non-standard image data where standard RGB-trained models often fail. Our expertise covers multi-dimensional data (3D, 4D), mmWave, advanced RADAR, and ultrasound. A core capability is bridging the "sim-to-real" gap using domain adaptation to leverage simulated data for real-world performance.

We treat domain shift as a first-class problem. Our approach is multi-faceted: we start with rigorous data analysis to quantify the shift between your source (e.g., simulation, lab data) and target (real-world) domains. We then employ a range of advanced domain adaptation techniques, from unsupervised methods like feature alignment to semi-supervised approaches that strategically leverage small amounts of labeled target data for maximum impact.

Robustness in a security context goes beyond simple accuracy. We focus on building systems that are resilient to adversarial conditions. This includes extensive data augmentation that simulates real-world noise and sensor variations, and rigorous testing for out-of-distribution detection.

We view simulated data as a powerful strategic asset. Our process starts by collaborating with your team to understand the simulation's parameters and limitations. We then use this data for rapid initial prototyping and architecture selection. The core of our work is bridging the "sim-to-real" gap using domain adaptation, ensuring the knowledge learned from millions of simulated data points transfers effectively to the sparse, real-world data from your physical sensor.

Yes. Often, the first step to solving a problem is defining it correctly. A key part of our initial engagement is to establish a robust, reproducible analytics and validation framework. We help you move beyond simple accuracy to define mission-specific metrics that truly reflect the required real-world performance of your system.

Depending on the requirements we design for the edge from day one. This involves selecting hardware-aware architectures and employing advanced optimization techniques like network pruning, quantization, and knowledge distillation to create highly efficient models. We ensure a seamless balance between state-of-the-art performance and the practical run-time and memory constraints of your target hardware.