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 OCI Data Labeling?

OCI Data Labeling serves as a robust solution for developers and data scientists aiming to generate accurately labeled datasets that are crucial for training artificial intelligence and machine learning models. This versatile tool supports multiple formats, including documents like PDF and TIFF, images such as JPEG and PNG, and various text types, allowing users to upload raw data, apply a range of annotations—like classification labels, object-detection bounding boxes, or key-value pairs—and export the annotated outputs in line-delimited JSON format, which is beneficial for the model-training workflow. Additionally, it offers customizable templates specifically designed for different types of annotations, along with user-friendly interfaces and public APIs that streamline the process of dataset creation and management. The service also ensures smooth interoperability with other data and AI tools, permitting the direct integration of annotated data into custom vision or language models, alongside Oracle’s AI solutions. Users can efficiently utilize OCI Data Labeling to build datasets, create records, annotate them, and then use the exported snapshots for robust model development, guaranteeing a seamless transition from data labeling to AI model training. As a result, this service significantly boosts the productivity of teams engaged in AI projects, ultimately fostering more efficient workflows and innovative applications.

Media

No images available

Media

Integrations Supported

Kili Technology
Labelbox
SuperAnnotate
V7 Darwin

Integrations Supported

JSON
Oracle AI Agent Platform
Oracle Cloud Infrastructure
Oracle Data Science

API Availability

API Availability

Has API

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

$0.0002 per 1,000 transactions
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

People For AI

Date Founded

2020

Company Location

France

Company Website

peopleforai.com

Company Facts

Organization Name

Oracle

Date Founded

1977

Company Location

United States

Company Website

www.oracle.com/artificial-intelligence/data-labeling/

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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