For employers

Hire this AI Trainer

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

Invite to Job
E
Egad Technologies

Egad Technologies

Agency
India flagCoimbatore, India
$18.00/hrExpert20+GDPR

Key Skills

Software

CVATCVAT
LabelboxLabelbox
Label StudioLabel Studio
MercorMercor
OneFormaOneForma
RemotasksRemotasks
RoboflowRoboflow
Scale AIScale AI

Top Subject Matter

No subject matter listed

Top Data Types

ImageImage
TextText
VideoVideo

Top Task Types

Bounding BoxBounding Box
ClassificationClassification
Emotion RecognitionEmotion Recognition
MappingMapping
PolygonPolygon

Company Overview

Egad Technologies is a full-service Business Process and Technology Solutions Provider headquartered in Coimbatore, India. Since its establishment in 2018, the company has grown into a trusted outsourcing and AI data services partner for global enterprises, AI platforms, and research teams. Egad Technologies delivers scalable, cost-effective, and quality-driven services across Data Annotation & AI Training, KPO, BPO, Digital Marketing, and IT Services, helping organizations improve operational efficiency and accelerate AI model development with production-ready datasets.

ExpertEnglishTamil

Security

Security Overview

Security & Privacy Overview Egad Technologies follows strict security and privacy controls to safeguard client data, intellectual property, and project confidentiality. Physical Security Controlled office access with restricted entry CCTV surveillance covering work areas Secure, designated workstations for production teams No unauthorized personal devices in secure project zones (where client-mandated) Cybersecurity Measures Secured network infrastructure with firewalls and endpoint protection Antivirus and malware protection on all systems Role-based access control to tools, datasets, and client platforms Secure cloud-based annotation platforms (e.g., Label Studio, CVAT, Roboflow) Regular password rotation and access audits Data Privacy & Confidentiality Mandatory Non-Disclosure Agreements (NDAs) for all employees and contractors Data privacy and information security training for all team members Client data accessed strictly on a need-to-know basis No local storage or external data transfer without authorization Audits & Compliance Internal quality and security reviews conducted periodically Client-specific security guidelines strictly followed Continuous monitoring of annotation accuracy, access logs, and compliance requirements Egad Technologies is committed to maintaining a secure, compliant, and trusted environment for all AI data annotation and training projects.

Security Credentials

GDPR

Labeling Experience

Roboflow

Upwork Client

RoboflowImageBounding Box
1. Bounding Box Annotation Precisely drew tight, pixel-accurate bounding boxes around target objects in each image Ensured consistent box alignment, minimal background inclusion, and object completeness Handled multiple objects per image with correct class assignment Followed occlusion, truncation, and overlap rules as per client specifications Maintained annotation consistency across diverse lighting, angles, and image resolutions Categorized images into predefined classes and sub-classes Applied single-label and multi-label classification, depending on image context Validated class accuracy through multi-level review processes Ensured balanced class distribution to avoid model bias. Assigned descriptive and contextual tags to each image Tags captured object attributes, environmental context, and visual features Improved dataset searchability, filtering, and downstream analytics Maintained a standardized tagging taxonomy to ensure consistency.

1. Bounding Box Annotation Precisely drew tight, pixel-accurate bounding boxes around target objects in each image Ensured consistent box alignment, minimal background inclusion, and object completeness Handled multiple objects per image with correct class assignment Followed occlusion, truncation, and overlap rules as per client specifications Maintained annotation consistency across diverse lighting, angles, and image resolutions Categorized images into predefined classes and sub-classes Applied single-label and multi-label classification, depending on image context Validated class accuracy through multi-level review processes Ensured balanced class distribution to avoid model bias. Assigned descriptive and contextual tags to each image Tags captured object attributes, environmental context, and visual features Improved dataset searchability, filtering, and downstream analytics Maintained a standardized tagging taxonomy to ensure consistency.

2021 - 2022