iMerit Technology

Skeleton annotation captures how a body moves, not just that it exists. Unlike bounding boxes that only indicate presence, keypoints placed on each joint and connected into a pose help models understand joint angles, occlusions, and subtle posture signals like injury risk. This level of detail is critical in real-world applications. In rehab tech, it tracks recovery and movement quality. In sports analytics, it enables precise performance analysis. In AR/VR, it supports more natural interaction. And in embodied AI (robotics / physical AI), it helps systems interpret and respond to human motion more accurately. Read the full blog: https://lnkd.in/ggCVhmNy #ComputerVision #AngoHub #SkeletonTool #PoseEstimation #Annotation

What does it take to evaluate AI agents properly? This video shows how it’s done on AngoHub, from expert review to automated LLM-based evaluation, using a real example to make it concrete. • Extracting non-obvious risks from an Alphabet 10-K • Side-by-side: human scoring vs LLM scoring on the same task • How evaluation feedback is captured and reused to improve the next model version Built for teams working on agentic systems that need reliable, repeatable evaluation. If you are building one, watch this: https://lnkd.in/gik_cGC8 #AgentEvaluation #Angohub #AgenticAI #ModelEvaluation #HumanInTheLoop

Laser-based weeding robots require millimeter-level precision, but many models still rely on bounding boxes that miss true plant boundaries. In field conditions, this leads to misclassification and crop damage. What improves accuracy in these robotic systems: ▪️ Pixel-level semantic segmentation ▪️ High-quality annotation + human review iMerit supports this with ML-assisted workflows and expert-led quality control for agricultural robotics applications. Read the latest blog on why pixel-perfect semantic segmentation is critical for laser-based weeding: https://lnkd.in/gHqt9Mbe #ComputerVision #AgTech #AI #DataAnnotation #PrecisionAgriculture

Egocentric video datasets for robotics need more than raw footage. Models depend on clear task structure, consistent capture conditions, and annotations that reflect real actions. That means structured scenarios (kitchen, household tasks), consistent capture protocols, and data that preserves hand-object interactions, motion, and context for downstream tasks like pose estimation and activity understanding. iMerit delivers this through trained teams, standardized workflows, built-in QA, and secure data transfer pipelines to ensure safe handling of sensitive video data. Learn more: https://lnkd.in/g_QSWZ2r #AI #Robotics #ComputerVision #DataAnnotation

iMerit evaluates AI agents at the system level, across planning, tool calls, memory, and final outcomes. By grading full agent traces with structured human review, teams can benchmark performance, catch failure modes early, and run reliable regression testing before release. Structured outputs and trace-level scoring integrate directly into your evaluation harness for benchmarking, reward modeling, and continuous monitoring. Learn more: https://lnkd.in/gZFUJi_7 #AgentEvaluation #GenerativeAI #AIQuality #AIValidation #AIGovernance

Boundary errors aren’t small mistakes, they’re system failures waiting to happen. 3D semantic segmentation enables point-level understanding of object edges, helping autonomous systems make safer, more reliable decisions in real-world environments. Read the blog to learn more. #AutonomousSystems #ComputerVision #3DSegmentation #AI

Mental health data is one of the hardest types of data to work with in AI. It's subjective, multimodal, deeply personal, and heavily regulated. Getting it wrong in a clinical setting isn't just a model accuracy problem. From multimodal signal integration to expert-led clinical annotation, bias gaps, and HIPAA compliance, here's a breakdown of where Behavioral Health AI stands today and what it takes to get the data right. https://lnkd.in/g3qbjE_V #BehavioralHealthAI #HealthcareAI #DataAnnotation

Evaluating AI agents in production requires more than static benchmarks. Real performance comes down to how reliably agents complete workflows, use tools correctly, and escalate when needed. A behavior-driven approach focused on task success, tool-use correctness, and escalation quality helps reduce risk, improve reliability, and build production-ready agent systems. Read more: https://lnkd.in/g5dfpy7d #AIAgents #AgentEvaluation #HumanInTheLoop #MLOps #GenerativeAI

Physical AI is pushing robotics beyond scripted automation into systems that can perceive, reason, and act in real-world environments. By combining vision, language, and action, robots are starting to move from controlled settings into dynamic, unpredictable conditions. The real challenge is making sure what works in simulation holds up in reality, where safety, context, and human judgment become critical to reliable performance. Read the full piece to learn more: https://lnkd.in/gbQuE33g #PhysicalAI #Robotics #AutonomousSystems #ComputerVision 

Biosensor data is complex, but extracting meaningful signals doesn’t have to be. In this video, see how teams use Ango Hub to annotate multimodal biosensor data, from ECG and respiration to acceleration and oxygen saturation, with structured, high-quality outputs. ▪️ Highlight and classify events like apnea or falls with flexible annotation tools ▪️ Capture rich metadata using dropdowns, checkboxes, and custom inputs ▪️ Link events with relationships like start and end times for better context Watch the video: https://lnkd.in/gdWijhPr #MedicalAI #Biosensors #HealthcareAI #DataAnnotation #MachineLearning