Skip to content Skip to footer

Enhancing AI Precision: Loopernode's Expert
AI Data Annotation Services


Discover the true power of AI

Loopernode offers precise and comprehensive AI data annotation services, meticulously labeling diverse datasets to enhance machine learning model accuracy. Our team ensures accurate annotations, critical for training AI models to interpret and analyze complex data with efficiency. Trust Loopernode for top-tier AI data annotation services, optimizing your AI applications for seamless performance and actionable insights.


Loopernode optimizes AI data workflows, maximizingproductivity for seamless data processing and insightsgeneration.


Loopernode ensures rigorous data protection measures, safeguarding valuable information and privacy for AI-driven insights and solutions.

Industrial experts

AI data annotation experts are adept at providing precise and nuanced labels, critical for training AI models to interpret and analyze complex datasets accurately.
what we do

We develop & build digital future

Loopernode provides vital functionalities for the automated pre-annotation of custom-tailored corpora and datasets. These features cover annotation for
various types of data, including text, audio, images, and videos. Additionally, Loopernode facilitates the creation, collection, and pre-collection of datasets.

Linguistic Services

Linguistic annotation services involve systematically analyzing and tagging linguistic elements in text or speech data, providing valuable insights for natural language processing applications and research. These annotations may include part-of-speech tagging, syntactic parsing, named entity recognition, sentiment analysis, and more, aiding in the development and refinement of language-focused AI models and systems. Linguistic service include :-

Part-of-Speech Tagging (POS Tagging)

Assigning grammatical parts of speech (e.g., noun, verb, adjective) to each word in a sentence, aiding in syntactic and semantic analysis.

Syntactic Parsing

Analyzing the sentence structure to identify phrases and dependencies between words, enhancing understanding of grammatical relationships.

Semantic Role Labeling (SRL)

Identifying the semantic roles of words in a sentence (e.g., agent, patient, theme), aiding in understanding the meaning of a sentence.

Named Entity Recognition (NER)

Identifying and categorizing named entities (e.g., names of persons, organizations, locations) within the text.

Sentiment Analysis

Determining the sentiment or emotional tone conveyed in a text, such as positive, negative, or neutral, to gauge attitudes or opinions.

Phonetic Annotation

Determining the sentiment or emotional tone conveyed in a text, such as positive, negative, or neutral, to gauge attitudes or opinions.

Dialect and Variability Annotation

Annotating dialectal, regional, or socio-linguistic variations in language usage.

Multilingual Annotation

Annotating text in multiple languages to facilitate multilingual natural language processing tasks.

Text Annotation Service

Text annotation service involves the process of adding specific and meaningful information to raw text data to enhance its structure, usability, and analysis. This service plays a critical role in training and improving natural language processing (NLP) models and other AI algorithms that rely on text data. The goal is to make unstructured text data more structured and understandable for machines.

Entity Recognition and Labeling

Identifying and marking specific entities within the text, such as names of people, organizations, locations, dates, quantities, or any other significant information. This is essential for applications like named entity recognition (NER) and information extraction.

Part-of-Speech Tagging

Assigning grammatical parts of speech (e.g., nouns, verbs, adjectives) to each word in the text. This helps in syntactic analysis, language understanding, and grammar-based applications.

Sentiment Analysis Labeling

Assigning sentiment labels (positive, negative, neutral) to sentences or phrases to determine the overall sentiment expressed in the text. It's crucial for understanding public opinion, customer feedback analysis, and brand monitoring.

Text Categorization

Categorizing or labeling text into predefined categories or classes based on its content. This is used in content organization, news categorization, and recommendation systems.

Intent Analysis

Identifying the intent or purpose behind a piece of text, which is valuable for chatbots, virtual assistants, and customer support systems to provide appropriate responses.

Image Annotation Services

Image annotation services involve the process of labeling or marking images with metadata, tags, or other notations to provide contextual information and make the images understandable and usable for machine learning algorithms. These annotations help train artificial intelligence (AI) models to recognize and comprehend objects, patterns, or features within images.

Object Detection

In this annotation type, bounding boxes are drawn around specific objects or regions of interest within an image. This allows AI models to identify and localize various objects within the image.

Semantic Segmentation

Semantic segmentation involves labeling each pixel in an image to classify the region to which it belongs. This annotation helps AI models understand the boundaries and relationships between different parts of an image.

Instance Segmentation

Similar to semantic segmentation, instance segmentation annotates each pixel in an image to identify individual instances of objects. This is particularly useful in scenarios where multiple objects of the same class exist and need to be differentiated.

Polygon Annotation

This annotation involves outlining objects or regions in an image using polygon shapes. It is commonly used for precise delineation of objects with irregular shapes.

Bounding Box Regression

This type of annotation involves adjusting and refining bounding boxes to provide more accurate localization of objects, improving the model's accuracy and precision.

Landmark Annotation

Landmark annotation involves marking specific points or landmarks on objects, which is valuable in tasks like facial landmark detection or pose estimation.

Image Classification

Image classification annotation assigns a label or category to an entire image based on its content. It helps AI models recognize and classify images into predefined classes.

Keypoint Detection

Keypoint annotation involves marking specific points of interest within an image, which is critical for tasks like human pose estimation, tracking, or gesture recognition.
Why choose us

Why Choose Loopernode for AI Data Annotation Services

At Loopernode, we stand as your trusted partner in AI data annotation, committed to delivering exceptional quality and precision in every annotation project we undertake. Here’s why choosing Loopernode for AI data annotation services sets you on the path to success.