Available for Consultation
Available for Consultation
Available for Consultation
metric Hive
Bold Ideas
Real Impact
Driven
From camera to insights, we build real-time computer vision pipelines that scale across enterprise environments
Statsyuk
Sports AIM
JumboTron
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We engineer production-grade computer vision solutions that turn visual chaos into structured business data - with precision, speed, and edge-native performance.
We engineer production-grade computer vision solutions that turn visual chaos into structured business data - with precision, speed, and edge-native performance.
Perception Systems
Visual Analytics
OCR
Synthetic Data
Pose Estimation
Anomaly Detection
(Intro)
Meet Harsh
The Founder
Harsh Raj is the Co-Head of Computer Vision, focused on building real-time, production-grade computer vision systems for sports and applied AI. An IITian, he currently serves as CTO at Real Ballers, a basketball analytics company, where he leads the end-to-end computer vision stack, including player and ball tracking, 3D and homography-based analytics, and real-time inference pipelines. He also works as a 3D Computer Vision Consultant for sports tech companies, has contributed to ISRO-sponsored projects, and brings additional domain expertise as a professional biomechanics analyst, bridging visual perception with real-world motion and performance analysis.
(Intro)
Meet Harsh
The Founder
Harsh Raj is the Co-Head of Computer Vision, focused on building real-time, production-grade computer vision systems for sports and applied AI. An IITian, he currently serves as CTO at Real Ballers, a basketball analytics company, where he leads the end-to-end computer vision stack, including player and ball tracking, 3D and homography-based analytics, and real-time inference pipelines. He also works as a 3D Computer Vision Consultant for sports tech companies, has contributed to ISRO-sponsored projects, and brings additional domain expertise as a professional biomechanics analyst, bridging visual perception with real-world motion and performance analysis.
(Intro)
Meet Chandan
The Founder
Chandan Kumar is a Computer Vision Engineer focused on building robust, real-time computer vision systems for sports analytics and applied AI. An IIT Kharagpur (Physics) graduate, Class of 2025, he works on end-to-end vision pipelines including object detection and multi-object tracking, homography-based analytics, and performance-optimized video intelligence systems under challenging real-world conditions such as motion blur, occlusions, and camera movement. He has collaborated with sports technology teams and startups on the design and evaluation of production-ready computer vision architectures, with a strong interest in bridging research insight with scalable, real-world deployment.
(Intro)
Meet Chandan
The Founder
Chandan Kumar is a Computer Vision Engineer focused on building robust, real-time computer vision systems for sports analytics and applied AI. An IIT Kharagpur (Physics) graduate, Class of 2025, he works on end-to-end vision pipelines including object detection and multi-object tracking, homography-based analytics, and performance-optimized video intelligence systems under challenging real-world conditions such as motion blur, occlusions, and camera movement. He has collaborated with sports technology teams and startups on the design and evaluation of production-ready computer vision architectures, with a strong interest in bridging research insight with scalable, real-world deployment.
(Intro)
Meet Chandan
The Founder
Chandan Kumar is a Computer Vision Engineer focused on building robust, real-time computer vision systems for sports analytics and applied AI. An IIT Kharagpur (Physics) graduate, Class of 2025, he works on end-to-end vision pipelines including object detection and multi-object tracking, homography-based analytics, and performance-optimized video intelligence systems under challenging real-world conditions such as motion blur, occlusions, and camera movement. He has collaborated with sports technology teams and startups on the design and evaluation of production-ready computer vision architectures, with a strong interest in bridging research insight with scalable, real-world deployment.
3D Computer Vision
Tracking
Model Optimization
3D Computer Vision
Tracking
Model Optimization
Pose Estimation
Edge Deployment
Reinforcement Learning
Pose Estimation
Edge Deployment
3D Computer Vision
Tracking
Model Optimization
3D Computer Vision
Tracking
Model Optimization
Pose Estimation
Edge Deployment
Reinforcement Learning
Pose Estimation
Edge Deployment
Project In Mind?
Get In Touch
Tell us about your project — we’ll bring the tools, vision, and energy to make it real.
Project In Mind?
Get In Touch
Tell us about your project — we’ll bring the tools, vision, and energy to make it real.
Project In Mind?
Get In Touch
Tell us about your project — we’ll bring the tools, vision, and energy to make it real.
(FAQs)
Your Questions, Answered
Helping you understand how we mitigate risk and deploy Edge AI.
Why Metric Hive vs. an in-house Engineer?
How does the process work?
We work in rigid 2-week sprints. It starts with a 5-Day Feasibility Audit (Go/No-Go check). If viable, we move to a 4-Week Pilot to prove the model on your hardware. Finally, we execute Production Deployment with full integration.
Do I need a massive dataset to start?
What hardware do you support?
What if the model isn't accurate enough?
Who owns the Intellectual Property (IP)?
(FAQs)
Your Questions, Answered
Helping you understand how we mitigate risk and deploy Edge AI.
Why Metric Hive vs. an in-house Engineer?
How does the process work?
We work in rigid 2-week sprints. It starts with a 5-Day Feasibility Audit (Go/No-Go check). If viable, we move to a 4-Week Pilot to prove the model on your hardware. Finally, we execute Production Deployment with full integration.
Do I need a massive dataset to start?
What hardware do you support?
What if the model isn't accurate enough?
Who owns the Intellectual Property (IP)?
(FAQs)
Your Questions, Answered
Helping you understand how we mitigate risk and deploy Edge AI.
Why Metric Hive vs. an in-house Engineer?
How does the process work?
We work in rigid 2-week sprints. It starts with a 5-Day Feasibility Audit (Go/No-Go check). If viable, we move to a 4-Week Pilot to prove the model on your hardware. Finally, we execute Production Deployment with full integration.
Do I need a massive dataset to start?
What hardware do you support?
What if the model isn't accurate enough?
Who owns the Intellectual Property (IP)?
Let's Connect
Let's Connect
Let's Connect