For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

LoginCreate account
Invite to Job
FitnGro

FitnGro

Agency
INDIA flag
Coimbatore, India
fitngro.com/
$10.00/hrIntermediate5+

Key Skills

Software

AWS SageMakerAWS SageMaker
CVATCVAT
Label StudioLabel Studio
SuperAnnotateSuperAnnotate
Surge AISurge AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code ProgrammingComputer Code Programming
ImageImage
TextText

Top Label Types

Computer Programming Coding
Fine Tuning
Object Detection
Prompt Response Writing SFT
Text Generation

Company Overview

We are a focused AI data annotation company delivering high-quality labeling and dataset support for machine learning applications. Our mission is to provide accurate, structured, and reliable training data that helps AI models perform effectively in real-world scenarios. Our services include image and video annotation, bounding boxes, segmentation, pose estimation labeling, text classification, audio transcription, and AI response evaluation. We also provide dataset validation and quality review to ensure consistency and precision. We operate with a dedicated team of 5 trained annotation specialists who follow strict quality control processes and data confidentiality standards. Our workflow includes multi-level review systems to maintain high accuracy and fast turnaround times. We support industries such as computer vision, fitness technology, health-tech, and NLP-based AI systems. Our focus is on reliability, data security, and scalable project execution for startups and growing AI companies.

IntermediateEnglishTamil

Security

Security Overview

We prioritize data security and confidentiality in all our operations. Our team follows strict data handling protocols, including controlled access to datasets, role-based permissions, and secure file transfer systems. All project data is stored in protected environments with restricted access to authorized personnel only. Each team member signs confidentiality agreements and follows structured annotation guidelines to ensure data integrity and compliance with client requirements. We operate under privacy-first principles aligned with global data protection standards such as GDPR best practices. We do not retain client data beyond project requirements, and all datasets are securely deleted upon completion unless otherwise specified. Our workflows include quality control audits and internal review processes to maintain accuracy, accountability, and traceability. Security awareness training is provided to all team members to ensure responsible handling of sensitive information.

Labeling Experience

CVAT

Human Pose Estimation & Fitness Exercise Annotation Project

CVATVideoSegmentationClassification
This project involved annotating image and video datasets for human pose estimation and exercise form analysis models. The objective was to support the development of AI systems capable of detecting repetitions, identifying incorrect posture, and analyzing body joint angles in fitness activities. The team performed keypoint annotation for major body joints (shoulders, elbows, hips, knees, ankles), action classification for multiple exercise categories, and quality validation of pose tracking outputs. The dataset included short exercise clips and static image frames extracted from workout sessions. Quality control measures included multi-level review workflows, random sampling audits, annotation guideline documentation, and inter-annotator agreement checks to ensure high consistency and labeling accuracy. Strict data privacy protocols were maintained throughout the project lifecycle.

This project involved annotating image and video datasets for human pose estimation and exercise form analysis models. The objective was to support the development of AI systems capable of detecting repetitions, identifying incorrect posture, and analyzing body joint angles in fitness activities. The team performed keypoint annotation for major body joints (shoulders, elbows, hips, knees, ankles), action classification for multiple exercise categories, and quality validation of pose tracking outputs. The dataset included short exercise clips and static image frames extracted from workout sessions. Quality control measures included multi-level review workflows, random sampling audits, annotation guideline documentation, and inter-annotator agreement checks to ensure high consistency and labeling accuracy. Strict data privacy protocols were maintained throughout the project lifecycle.

2024 - 2025