Available for Consultation

Available for Consultation

Available for Consultation

Trusted by founders, smart Enterprises and more.

Actionable

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Intelligence

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Smart Industry

From camera to insights, we build real-time computer vision pipelines that scale across enterprise environments

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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

3D Spatial Vision

Tracking & Localization

Pose Estimation

Real-Time AI

(Why clients love Metric Hive)

Testimonials

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$50k

$50k

Monthly Revenue Recovered

18%

18%

Defect Detection Rate

<80ms

<80ms

Inference Latency

Recent Works

people playing ice hockey on stadium

A production-ready tracking system built for the toughest conditions in computer vision: high-speed play, heavy motion blur, and aggressive camera pans.

01

/ 03

Ice Hockey Player Tracking

Ornate building with outdoor cafe seating in sunlight.

Status

Production-Ready

Tech Stack

Custom CV Pipeline

Features

Camera Stabilization

Mask Propagation

Jersey Recognition

Multi-Object Tracking (MOT)

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people playing ice hockey on stadium

A production-ready tracking system built for the toughest conditions in computer vision: high-speed play, heavy motion blur, and aggressive camera pans.

01

/ 03

Ice Hockey Player Tracking

Ornate building with outdoor cafe seating in sunlight.

Status

Production-Ready

Tech Stack

Custom CV Pipeline

Features

Camera Stabilization

Mask Propagation

Jersey Recognition

Multi-Object Tracking (MOT)

noise
people playing ice hockey on stadium

A production-ready tracking system built for the toughest conditions in computer vision: high-speed play, heavy motion blur, and aggressive camera pans.

01

/ 03

Ice Hockey Player Tracking

Ornate building with outdoor cafe seating in sunlight.

Status

Production-Ready

Tech Stack

Custom CV Pipeline

Features

Camera Stabilization

Mask Propagation

Jersey Recognition

Multi-Object Tracking (MOT)

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aerial photo of football field during daytime

An R&D deep dive into Meta's SAM 3D release. We tested the new "Concept-Centric" architecture on high-complexity American Football scenes to evaluate the distinctions between static scene generation and dynamic body rigging.

02

/ 03

Player 3D Pose R&D

Cover Image

Status

R&D / Prototype

Focus

3D Reconstruction

Features

3D Computer Vision

Biomechanics

Flow Matching

Momentum Human Rig (MHR)

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aerial photo of football field during daytime

An R&D deep dive into Meta's SAM 3D release. We tested the new "Concept-Centric" architecture on high-complexity American Football scenes to evaluate the distinctions between static scene generation and dynamic body rigging.

02

/ 03

Player 3D Pose R&D

Cover Image

Status

R&D / Prototype

Focus

3D Reconstruction

Features

3D Computer Vision

Biomechanics

Flow Matching

Momentum Human Rig (MHR)

noise
aerial photo of football field during daytime

An R&D deep dive into Meta's SAM 3D release. We tested the new "Concept-Centric" architecture on high-complexity American Football scenes to evaluate the distinctions between static scene generation and dynamic body rigging.

02

/ 03

Player 3D Pose R&D

Cover Image

Status

R&D / Prototype

Focus

3D Reconstruction

Features

3D Computer Vision

Biomechanics

Flow Matching

Momentum Human Rig (MHR)

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grayscale photography of person holding racket

A seamless fusion of sports analytics and AI. This system acts as a "digital referee," utilizing custom optimized models to track the ball, players, and game events in real-time on midrange hardware.

03

/ 03

Padel Game Analysis

Cover Image

Status

Production-Ready

Tech Stack

End-to-End CV Pipeline

Features

TrackNetV3

RTMO (Pose Estimation)

Handedness Detection

Shot Detection

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grayscale photography of person holding racket

A seamless fusion of sports analytics and AI. This system acts as a "digital referee," utilizing custom optimized models to track the ball, players, and game events in real-time on midrange hardware.

03

/ 03

Padel Game Analysis

Cover Image

Status

Production-Ready

Tech Stack

End-to-End CV Pipeline

Features

TrackNetV3

RTMO (Pose Estimation)

Handedness Detection

Shot Detection

noise
grayscale photography of person holding racket

A seamless fusion of sports analytics and AI. This system acts as a "digital referee," utilizing custom optimized models to track the ball, players, and game events in real-time on midrange hardware.

03

/ 03

Padel Game Analysis

Cover Image

Status

Production-Ready

Tech Stack

End-to-End CV Pipeline

Features

TrackNetV3

RTMO (Pose Estimation)

Handedness Detection

Shot Detection

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(Intro)

Meet Harsh

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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

man-image

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

man-image

The Founder

Harsh Raj Co-Head of Computer Vision | CTO, Real Ballers | 3D Computer Vision Consultant (Sports Tech) | Professional Biomechanics Analyst | ISRO-Sponsored Projects Contributor

(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.

man-image

(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.

man-image

(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.

man-image

(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

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Speak With an AI Expert

Let's make something happen together

Let's Connect

MacBook Pro turned on

Speak With an AI Expert

Let's make something happen together

Let's Connect

MacBook Pro turned on

Speak With an AI Expert

Let's make something happen together