# Vision systems that get sharper the longer they run. Most vision models lose accuracy in real conditions: changing lighting, occlusion, sensor drift. We build, train, and operate computer vision systems that hold their accuracy in the field and improve with use, across imagery, video, LiDAR, and live streams. A real-time vision system that detects foreleg lameness in racehorses from a multi-camera array. ## CHALLENGES ### **What we typically see** The pattern repeats across industries. Cameras, scanners, and sensors capture more than any team can review, and the most expensive failures hide in that gap. ### Visual review that doesn't scale Manual inspection of images and video breaks down at volume. Accuracy drops and cost climbs faster than headcount can absorb. ### Problems caught too late The defect, the safety incident, the equipment failure. Each is cheaper to catch as it happens than to address downstream. Most operations still find out too late. ### Footage that goes unanalyzed Most enterprises produce more visual data in a day than their teams can review in a year. The footage is stored. The intelligence inside it is not. ### Judgments that vary by reviewer Two trained operators looking at the same image can reach different conclusions. The variance shows up as a quality, compliance, and defensibility risk that a consistent decision rule resolves. ## Approach We build vision systems trained on domain-specific data and optimized for the environment where inference happens. 01. Object detection, classification, and segmentation 02. Annotation, augmentation, and synthetic data pipelines 03. Architecture tuned to latency, accuracy, and memory budget 04. Edge, cloud, or hybrid deployment wired into existing workflows The output is vision models your team can operate and extend, along with the data infrastructure, training environment, and operational practices that make subsequent builds shorter and cheaper than the first. ### "We built out vision systems to better predict the risk of injury for animals. We implemented hardware and software to track biomechanical points in real-time and pass those off to vets so they can intervene right away." Jacob Zweig Managing Director, AI ## Applications ### Across industries and teams #### Quality Inspection Built for manufacturers confirming assembly, finish, and tolerance compliance at line speed #### Defect Detection Built for industrial operators catching surface, structural, and process anomalies before shipment #### Safety Monitoring Built for operations and safety teams flagging hazards, PPE compliance, and unsafe behavior in real time #### Behavior & Activity Recognition Built for teams quantifying movement, posture, or sequence across sports, workforce, healthcare, and animal monitoring #### Medical Imaging Diagnosis Built for healthcare and life sciences teams accelerating diagnostic workflows, image triage, and clinical research #### Yield & Inventory Counting Built for agriculture, retail, and supply chain operators automating physical counts ## Proof & Perspective ### From the field Innovative thinking. Real outcomes. #### Vision AI Enhancing packaging quality control with computer vision .png) #### Vision AI Leveraging computer vision to identify animals at risk of injury ## FAQ ### How much labeled data do we need to start? Less than most teams assume. A few hundred to a few thousand carefully chosen examples per class is often enough to begin, with augmentation, synthetic data, and pretrained backbones closing the rest of the gap. What matters is that the labels represent the conditions you expect in production. ### Can we use the cameras and infrastructure we already have? Usually, yes. Most vision systems can be built on existing camera feeds and storage. We assess what is in place during scoping and only recommend new hardware when the workload requires resolution, frame rate, or sensor types the existing setup cannot deliver. ### What happens when conditions change after deployment? Models drift. Lighting shifts, equipment ages, targets evolve. Our deployments include drift monitoring that flags when inputs or outputs move outside acceptable ranges, plus a retraining loop that feeds reviewed cases back into the model so accuracy holds over time. ### How do you decide between edge, cloud, or hybrid deployment? Latency, cost, and connectivity drive the choice. Edge fits real-time use cases and low-bandwidth sites. Cloud fits heavier offline analysis. Hybrid runs detection at the edge and analytics in the cloud. We pick the configuration the workload requires. ### How do you handle privacy and compliance when the model sees people? Most use cases do not require identifying individuals, so we default to anonymized outputs (counts, postures, trajectories) rather than recognizable footage. Where identification is required, the system operates within regional regulation such as GDPR, HIPAA, and BIPA, with retention controls and audit trails engineered in.