iMerit Technology

CVPR attendees, this one's for you. Stop by iMerit Booth #535 and enter our raffle to win a LEGO Star Wars™ N-1 Starfighter™ set or a Meta Quest 3S. Winners announced June 5 & 6 at 5 PM. Note: Must be present during the raffle drawing to win. #CVPR2026 #AI #Robotics #GenAI

Heading to CVPR 2026? Meet the iMerit experts in robotics, GenAI, multimodal evaluation, and physical AI systems. Find us at Booth #535 | June 3–7 | Denver, CO #CVPR2026 #PhysicalAI #Robotics #MultimodalAI

Healthcare AI depends on usable clinical data, but de-identification now goes far beyond removing names or IDs. Challenges include: • Re-identification risks • AI-assisted PHI detection • Maintaining clinical utility Read here: imerit.ai/resources/blog… #HealthcareAI #Deid

Heading to @Robotics_Summit & Expo? Find iMerit at Booth 629 to talk robotics data workflows for real-world deployment; from 3D point clouds and sensor fusion to object tracking and egocentric video data collection. imerit.ai/domains/roboti… #Robotics #EmbodiedAI #RoboticsSummit

Weeding robots rarely fail because of the algorithm. They fail because training data was labeled without agronomy expertise. Distinguishing barnyard grass from rice seedlings is what makes field performance possible. Read: imerit.net/resources/blog… #AgriTech #PrecisionAgriculture

#EmbodiedAI needs data from inside the action. Egocentric video helps models learn how humans interact with objects & environments in the real world, but collecting scalable, high-quality training data is complex. How iMerit approaches it: imerit.ai/resources/blog… #datacollection

iMerit will be at #CVPR2026 in Denver, June 3–7. Building multimodal systems, embodied AI, or perception pipelines? Let’s connect. From ground truth quality & edge case coverage to scaling data for foundation model development, meet us at Booth 535. #ComputerVision #EmbodiedAI

iMerit CEO & Founder, Radha Basu, has been listed in @AIMmediahouse's Next 15 AI CEOs in America 2026, recognizing leaders driving enterprise AI from experimentation to production across infrastructure, applications, & #AI services. List: aimmediahouse.com/lists/aim-next… #EnterpriseAI

Simulation environments fail when they never learn from real-world perception errors. Closed-loop HD mapping turns edge-case failures into annotated simulation updates and validated improvements. Read the blog here: imerit.ai/resources/blog… #AutonomousDriving #HDMaps #LiDAR

Meet iMerit at @Robotics_Summit & Expo in Boston. From sensor fusion to embodied AI, robotics systems rely on production-ready data workflows. Connect with our team at Booth 629. #RoboticsSummit #EmbodiedAI #AIData

AI training tasks demand different levels of human expertise. From labeling to RLHF, red teaming, RAG evaluation & expert review, weak reviewer-task alignment degrades model quality. Read more: imerit.ai/resources/blog… #AITraining #RLHF #HumanInTheLoop #RLHF #ExpertAnnotation

iMerit has been named to the 2026 @IAOP® Global 100, recognizing leading solution providers, business leaders, and advisors worldwide. We also earned 2 stars for excellence in client references and CSR. View full list: iaop.org/Content/19/165… #Global100 #OWS26 #IAOP

Crop rows are messy, variable, and often unpredictable for robots to interpret. High-quality annotation helps weeding robots localize weeds more accurately across real agricultural conditions. imerit.net/domains/agricu… #AgricultureAI #AgTech #PrecisionAgriculture #Robotics

Most #AI discussions focus on models & benchmarks. But in production, the real challenge is keeping systems reliable as data shifts, edge cases grow, and errors slip through without review. What production AI actually depends on: imerit.net/resources/blog… #MLOps #MachineLearning

Humanoid robots struggle when training data doesn’t reflect how tasks actually happen at home. iMerit built a 200-hour first-person household dataset using Meta Quest 3 with step-level action and object labeling. Read here: imerit.net/resources/case… #HumanoidRobots #RobotTraining

In a @HlthcareToday article, Sina Bari explains why medical AI depends on shared, high-quality data. Without common benchmarks, results are hard to compare, and progress slows. Take a look: healthcaretoday.com/article/commen… #HealthcareAI #OpenData #MedicalAI

Bounding boxes often pull in the background, so models learn noise along with the signal. Segmentation labels every pixel, keeping boundaries clean and improving performance in overlap and occlusion. Read more: bit.ly/4d4AHaF #ComputerVision #SemanticSegmentation #AI

Urban #LiDAR struggles in cities because of data gaps. Hidden objects, weak detail at distance, and noisy signals all impact detection. Fixing it means better coverage, cleaner annotation, and aligned sensor data. Read: imerit.net/resources/blog… #AutonomousDriving #ComputerVision

Skeleton annotation captures how a body moves, not just that it exists. Unlike bounding boxes, keypoints + pose structure let models understand angles, occlusions, and posture signals. Used in rehab, sports, AR/VR, robotics. Read: imerit.net/resources/blog… #AngoHub #PoseEstimation

What does it take to evaluate AI agents properly? This video breaks it down on AngoHub: • Extracting hidden risks from an Alphabet 10-K • Human vs LLM scoring • Reusing eval data to improve models Watch: #AI" target="_blank" rel="nofollow noopener">youtu.be/jj-pJVOpmTg?si… #AIAgents #LLM #MLEvaluation #DataQuality