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Text Annotation Services

    Services We Deliver

    Klatch's Text Annotation Services

    We support clients with all text labeling needs, such as collecting and developing AI training datasets for computer vision systems and NLP solutions. Our goal is to enhance and annotate text precisely and securely, enabling your data projects to succeed.

    Text Classification

    The primary text annotation method classifies text based on content type, intent, sentiment, and subject. After categorizing the datasets, they are put into the system as part of a predetermined segment that machines may access to respond.

    Named Entity Recognition

    Klatch supports advancements in digital document analysis, conversational AI development, and knowledge base curation by finding, categorizing, highlighting, and connecting key text and metadata strings.

    Entity Linking

    While annotators collect things from extensive data sources, they must be connected to generate meaningful datasets. Klatch’s text annotation specialists can create comprehensive learning databases through categorization and end-to-end linking.

    Sentiment Analysis

    Klatch’s annotation specialists detect patterns in massive amounts of text datasets, such as product reviews, economic news, and social media. Annotation for sentiment analysis, available in any language, lets businesses discover how customers see their products, forecast stock prices, and more.

    Linguistic Annotation

    Textual dataset labeling, formerly known as corpus annotation, focuses on the linguistic characteristics of audio and texts, phonetic annotation, pieces of semantic annotation, POS tagging, and others. This approach is correct when it comes to the process of training models for machine translation.

    Intent Analysis

    Text annotation specialists from Klatch connect the NLU foundations to fuel the development of next-generation bots, digital assistants, and conversational AI solutions.

    Our Text Annotation services will help you project succeed. Let’s get started

    Industries we serve

    Industries benefiting from our Text Labeling solutions

    Our Text Annotation services are used across various sectors that use AI or machine learning-based models. We cover all industries, from retail to institutions and healthcare, with the same degree of attention and quality.

    • Healthcare

      Klatch provides pharmaceutical, medical device, health insurance, and network providers with HIPAA-compliant text annotation and collection solutions.

    • Social Media

      Klatch delivers UGC moderation, profanity filtration, text, image, and video moderation, and social media monitoring to improve brand reputation and user experience.

    • Commerce

      Katch delivers customer experience (CX) solutions to B2B, B2C, and D2C organisations to improve customer service, market research, and brand reputation.

    • Finance & Insurance

      Klatch helps financial institutions utilise machine learning models and RPA for greater productivity, customer experiences, and risk assessment by creating complex behavioral patterns to analyze.

    • Legal

      Automate contract reviews, extract dates, and speed up case law searches. Massive and complex legal documents require many hours to process. Text annotation helps automate and save time.

    • Government

      Klatch’s safe and controlled facilities offer reliable data processing services for federal, state, and local agencies. It helps institutions in using Machine Learning for public benefit.

    why choose us

    Why Klatch is the right partner

    Our text annotation propels you from experimental R&D prototypes to operation, production-ready solutions. We understand that your data training process is dynamic, and we strive to be as agile and adaptable as you require.

    • Quality with Accuracy

      Get the best-in-class quality services with the highest accuracy level, delivering excellent data annotation through multiple stages of auditing and reviewing labeled data.

    • Fully Scalable Workforce

      Work with an expert-degree prolonged workforce to annotate data as consistent with the call for a scalable answer with a turnaround time to satisfy the unique client’s needs.

    • Cost-Effective Pricing

      Outsourcing data annotations means that clients have access to cost-effective data annotation services that help minimize project costs and help them with the highest efficiency.

    • Security with Privacy

      We are certified and manage employees worldwide, all under strict NDA. We perform in monitored facilities for high-protection work, with strict protection protocols.


    Text Annotation process

    Our specialists and technology enable your project with capacity control, real-time quality monitoring, secured access, and streamlined team collaboration.

    • Expert Consultation

      Dynamic, solution-oriented strategy. Problem-solving through entensive annotation.

    • Training

      Targeted resources. Skilled specialists. Focused and in-depth formative assessment program. Domain knowledge. Drafting tools.

    • Process Customization

      Annotation tools and procedures must be in sync. Development milestones are structured on production processes and quality assurance in multiple phases.

    • Feedback Loop

      Analytics focused on providing transparency. Insights on service delivery and real-time tracking of the project. Model enhancement using dynamic simulation.

    • Assessment

      Deliverable assessment. Key metric assessment, as well as quality control procedures. Rethinking the model. Analyze business outcomes.


    Frequently asked questions about text annotation

    What is annotations in a text?

    Text annotation helps machine learning algorithms understand the text they are given. It is commonly used for sentiment analysis, part-of-speech tagging (POST), entity recognition (NER), and classification. Based on the project criteria, the text annotation process separates the data into appropriate groups, such as phrases, sentences, and keywords. The annotated datasets teach bots how to converse in natural human language. Text annotation is used to provide training data to improve the performance of search engines, build chatbots, create a system for answering questions, translate text from one language to another, and others.

    What is an example of a annotation?

    5 common annotation examples are:

    • Bounding Boxes: Development of self-driving cars are an data annotation example. Klatch annotators marked vehicles, bikes, and people in traffic. The annotated objects are placed into a machine-learning model to help the self-driving car tell them properly.
    • 3D cuboids: Klatch annotators draw a cube around the object of interest and put anchor points at each object’s edges. The annotator estimates where the edges would be based on the size and height of the object as well as the angle of the data.
    • Polygons: Klatch annotators draw lines by putting dots around the outside edge of the object of interest. The space between the dots is labeled with classes, such as cars, bikes, or trucks.
    • Semantic segmentation: Klatch annotators are provided datasets to label each structure with the correct name or to separate road infrastructure into vehicles, buildings, pedestrians, barriers, trees, and crosswalks.
    • Polylines: Autonomous vehicle technology can be taught to stay in the right lane without turning by annotating polylines on the road lanes and sidewalks.

    What are the three types of annotation?

    3 types of annotation are:

    • Text annotation
    • Image annotation
    • Video annotation

    Klatch Technologies caters to all kinds of annotation or data labeling requirements for any project size.


    What are 4 benefits of annotating?

    4 benefits of annotating are:

    • Autonomous transportation and technology: Klatch’s image labeling helps build datasets for teaching self-driving software to recognize road signs, bike lanes, pedestrians, traffic lights, objects in the setting that could be dangerous, and weather conditions.
    • Agriculture: Large farmers and agriculture companies use Klatch’s image annotation services to protect their crops from damage. Computer vision in agriculture helps keep an eye on crop health, detect weeds and pests, manage livestock and do geo-sensing.
    • Security and surveillance: Klatch’s image annotation is used by companies that make security cameras and video tools to create training datasets for crowd detection, thermal vision, traffic motion, theft detection, and pedestrian tracking.
    • Medical AI: Klatch provides medical image data to train ML models for pharmaceuticals, medical devices, and health insurance companies with HIPAA-compliant image annotation.
    • Retail and E-Commerce: Katch delivers image labeling and computer vision solutions to retail and e-commerce brands to improve customer service, market research, inventory management, trend forecasting, and brand reputation.
    • Financial services: Klatch prepares datasets for ML models and technology for insurance and financial services firms. The models help improve customer satisfaction and reduce policy claim time and risk assessment.
    • Manufacturing: Klatch’s image annotation allows manufacturing companies to recognize robot productivity and maximize production efficiency. Intelligent robots are used to aid in the detection of faulty products or defects in manufacturing.

    Is outsourcing text annotation projects safe?

    We assure your text data protection by combining streamlined text annotation operations and cross-quality checks. The results are supplied in a secure manner tailored to your specific requirements. Our data annotation teams adhere to a stringent nondisclosure agreement.

    Annotating text can be done in-house until there is too much datasets to handle. When things get hard, you’ll need specialists and the correct tools to label them accurately. For example, if you are developing a medical product, you will need a subject matter expert to classify objects found on an MRI report. Klatch data specialists can annotate datasets in various processes.

    How do you annotate text data?

    Klatch has a specialized staff of qualified data annotators and subject matter experts who use a tried-and-true combination of manual operations to label text. Our team of text annotators begins by comprehending the project specifications as laid forth by the client. As a result, they conform to the current annotation dashboard and terminology. To enhance the utility of training datasets, they use a customized technique to extract datasets and add suitable labels and attributes. What seperates us from the competition is that our founders handle your projects and give direction on formatting data.

    Why do we annotate text?

    We annotate text data as it is the first step in developing intelligent chatbots, virtual assistants, email filters, translators, and anything else that allows robots to comprehend and respond to humans’ natural processing language.

    What are the 5 types of annotation?

    5 types of annotation are:

    • Bounding boxes
    • 3D cuboid
    • Text classification
    • Landmark annotation
    • Semantic segmentation

    Klatch Technologies caters to all kinds of annotation or data labeling requirements for any project size.


    What type of annotations we use?

    The types of annotations we use are:

    • Object of interest detection: Object recognition is a crucial use in data annotation. In this step, the object is identified and labeled. A data annotation method is used to annotate these objects and make them detectable using computer vision.
    • Recognizing types of objects: It is essential to recognize what type of objects are identified. The diverse objects identified could be cars, bikers, street poles, sidewalks, pedestrians, trees, and buildings, as visible in the natural setting.
    • Objects classification: Models for machine-learning training entail that objects be classified. Data annotation utilizes several distinct methods to help an AI model’s detection and categorization of objects.
    • Semantic object segmentation: Segmentation annotation assigns each data pixel to a class. Data annotation allows semantic segmentation and helps segment items by category, placement, and attributes in a single class.
    • Recognizing human faces: In data annotation, people’s faces are annotated from one point to another, gauging the dimension of the face and its various facial features to feed facial recognition algorithms.

    What location are you in?

    Our corporate headquarters are in India. We have various offshoring centers in Asia and Africa. Our client support teams are available 24 hours a day, 7 days a week, and keep you up to date on the project’s status.

    Text Annotation Services Pricing

    We reformed the outsourcing model with long-term viability in mind. If you have a recurring need for data annotation, we can set up a dedicated team for you or work on a project basis.

    Full-time Annotators

    Designed for firms with recurring data outsourcing needs
    $495 /month
    Pricing are starting figures
    • All full-time annotators include:
    • A dedicated annotation specialist
    • Quality assurance audits
    • Custom shifts
    • Remote support

    Per Project

    A comprehensive plan for any project size or data needs.
    $0.05 /annotation
    Pricing are starting figures
    • All project plans services include:
    • High precision
    • Scalability is available immediately.
    • Dedicated to your deadlines
    • Remote support