People For AI Reviews & Ratings

People For AI

5.0 out of 5 stars

Ratings and Reviews 14 Ratings

Total 5 out of 5 stars
ease 4.5 out of 5 stars
features 4.5 out of 5 stars
design 4.5 out of 5 stars
support 5 out of 5 stars

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

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What is People For AI?

People For AI specializes in data labeling services designed to deliver exceptional datasets for the training of computer vision, natural language processing, or speech recognition systems. Our advanced AI-driven tools are specifically adapted to meet the unique requirements of your project, ensuring that your data is meticulously handled with the appropriate technology, skilled personnel, and effective strategies. We prioritize long-term labelers, which allows us to excel in providing high-quality data annotation, but we are also equipped to handle a diverse range of projects. For further information about our dedicated team of labelers and our comprehensive services, please explore our website. Additionally, we pride ourselves on maintaining a rigorous quality assurance process to guarantee the highest standards in data labeling.

What is LabelFort?

LabelFort, an all-encompassing data annotation platform created by Predusk AI, caters specifically to AI teams that need their training data to adhere to strict audit requirements. This innovative platform harnesses AI pre-labeling to streamline repetitive tasks, while one of the five available quality assurance workflows—ranging from single pass to double-blind reviews—guarantees that each entry is ultimately confirmed by a human. Each dataset concludes with a comprehensive evidence pack, which contains versioned pre-labels, a secure log that tracks the labeling, review, and approval processes, along with inter-annotator agreement metrics, all in accordance with compliance frameworks such as the EU AI Act, HIPAA, GDPR, SOC 2, and DPDP documentation. Users can annotate various data types—including images, videos, text, audio, LiDAR, DICOM, documents, and RLHF—utilizing a cohesive platform that features an annotation canvas, ontology and form builder, QA console, audit log viewer, model registry, and dataset management tools. With eight unique user roles that provide tailored routing and access protocols, teams can either operate the platform autonomously or enhance their capabilities by consulting with a trained domain specialist team through the Hire a Team service. This effective combination of automated AI processes and human validation ensures the integrity and quality of the data throughout the annotation process, supporting compliance and quality assurance in a robust manner. Additionally, the platform is continuously updated to incorporate user feedback, further enhancing its usability and effectiveness for diverse annotation needs.

Media

No images available

Media

No images available

Integrations Supported

Kili Technology
Labelbox
SuperAnnotate
V7 Darwin

Integrations Supported

API Availability

API Availability

Pricing Information

Free trials are available,
POC project (under 1000€) are availables.
Production project begin from 200 hours of labeling.
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Online Training

Company Facts

Organization Name

People For AI

Date Founded

2020

Company Location

France

Company Website

peopleforai.com

Company Facts

Organization Name

Predusk Technology Private Limited

Date Founded

2014

Company Location

India

Company Website

labelfort.ai/

Categories and Features

Data Annotation

Not specified

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
Team Collaboration

Categories and Features

Data Annotation

Not specified

Data Labeling

Not specified

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