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Trinode.ai

Trinode.ai

Agency
PAKISTAN flag
Islamabad, Pakistan
trinode.online
$20.00/hrEntry Level4+

Key Skills

Software

EncordEncord
V7 LabsV7 Labs
Internal/Proprietary Tooling
CVATCVAT

Top Subject Matter

No subject matter listed

Top Data Types

AudioAudio
Computer Code ProgrammingComputer Code Programming
ImageImage

Top Label Types

Bounding Box
Polyline
Prompt Response Writing SFT
Question Answering
Segmentation

Company Overview

We are a forward thinking tech company driven by innovation and powered by artificial intelligence. Our mission is to create digital solutions that help businesses grow, adapt, and thrive in an ever changing world. By blending creativity, technology, and intelligence, we deliver impactful experiences that push the boundaries of what’s possible.

Entry LevelEnglishUrdu

Security

Security Overview

Trinode follows industry best practices for data security and privacy, including strict access control, role based permissions, and secure handling of client data. All datasets are stored in encrypted environments with limited access granted only to authorized team members. We support secure data transfer protocols and follow GDPR-aligned data minimization and confidentiality principles. For sensitive and medical data, we ensure anonymization and controlled access throughout the labeling lifecycle. Trinode is actively working toward formal security certifications as the organization.

Labeling Experience

CVAT

Medical Image Annotation

CVATMedical DicomBounding BoxSegmentation
Conducted medical image annotation on anonymized DICOM datasets to support the training of deep learning models for healthcare applications. The project involved image classification, region-based bounding box annotations, and pixel level segmentation under strict quality guidelines. Quality assurance processes included multi pass review, consistency checks, and standardized annotation protocols to ensure high inter-annotator agreement and reliable training data.

Conducted medical image annotation on anonymized DICOM datasets to support the training of deep learning models for healthcare applications. The project involved image classification, region-based bounding box annotations, and pixel level segmentation under strict quality guidelines. Quality assurance processes included multi pass review, consistency checks, and standardized annotation protocols to ensure high inter-annotator agreement and reliable training data.

2025
CVAT

Image Based Object Detection & Segmentation

CVATImageBounding BoxSegmentation
Performed large scale image annotation for computer vision model training, focusing on object detection and semantic segmentation tasks. The project included precise bounding box annotations, class labeling, and pixel level segmentation across diverse image datasets. Annotation workflows followed structured guidelines with multi stage quality checks, reviewer validation and consistency audits to ensure high quality training data suitable for deep learning pipelines.

Performed large scale image annotation for computer vision model training, focusing on object detection and semantic segmentation tasks. The project included precise bounding box annotations, class labeling, and pixel level segmentation across diverse image datasets. Annotation workflows followed structured guidelines with multi stage quality checks, reviewer validation and consistency audits to ensure high quality training data suitable for deep learning pipelines.

2025 - 2025