Automate the data pipeline with Predictly

Build the entire data lifecycle starting from data collection to annotation with Predictly’s all-in-one data annotation platform which offers a better and faster way to annotate your data at ease.

Predictly’s AI assisted data annotation platform

(end-to-end workflow)

Data Collection and storage

Our platform’s data labeling starts with an analysis on the data available. If you don’t have data then don’t worry Predictly will help you to collect the right data. Predictly offers a wide range of data storage options to you.

Data Processing

Our data processing API helps you to process and clean the unstructured data and prepare it for the data annotation process.

Project setup and management

Once the data is ready, Our data annotation platform will create projects, assign annotators and track the progress automatically.

Data Labeling

Predictly offers both manual and automated way of annotation, to get the most of our platform we provide a human-in-loop annotation method which is based on Active learning.
A synchronization algorithm passes data points through multiple annotators to filter out mismatch labels.

Auto labeling in a loop

Auto labels allow users to annotate the data at scale. Our observation says auto labelling is 5x times faster than manual annotation.

Quality check

Predictly ensures the annotated data arrives at the user’s end with right labels that’s why we run a quality check method at the end to provide high quality and accurate annotated data.

Our platform Capabilities

Types of data annotation we offer

Natural Language Processing

Text classification is the process of classifying texts/documents into specific categories.

Machine translation allows you to translate one language to another language or languages.

POS tagging is the process of marking/tagging words in a sentence as corresponding to a particular part of speech.

NER is the process of identifying the named entities for the words/tokens in a sentence/text.

Relation extraction is the method of finding the relations between the named entities.

Dependency parsing is the way to analyze the grammatical structure of a sentence based on the dependencies of the words/tokens.

Question answering is a system which specifically tries to find out answers to the query. It can be both open domain and close domain.

Computer Vision

Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label. Typically, Image Classification refers to images in which only one object appears and is analyzed.

Object detection is a method in computer vision that deals with detecting instances of semantic objects of a certain class in digital images and videos.

segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.

Landmark detection is a fundamental building block of computer vision applications such as face recognition, pose recognition, emotion recognition, head recognition to put the crown on it, and many more in augmented reality

Optical character recognition or optical character reader is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo or from subtitle text superimposed on an image.


How you can use our Data annotation platform?

data security

Data security:

We believe that protecting your data is the most important part of our service, and we ensure that our customer’s data runs in a fully secured environment with proper encryption configurations.

Predictly is committed to abide by the laws and regulations of your country’s data protection laws and regulations.

Our Partners

Explore our Dataset Catalog