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Data Labeling: The process of reviewing and placing relevant, functional labels on data to gather insights

Our data labeling service includes annotating raw data, such as text, images or audio, to create labeled datasets used for training machine learning models. We help to improve model performance and robustness by providing a more comprehensive and varied training dataset.
data labeling

Benefits of Data Labeling

Implementing AI technology helps businesses capitalize on decision-making and insights gathered by processing data and Data Labeling plays a vital role in that process. 

Data Derivation

Labeling helps in the efficient retrieval of data required by resources for further processing. Our Data Labeling service helps improve model performance and robustness by providing a more comprehensive and varied training dataset. 

Structuring the Unstructured

Data Labeling undertakes data enrichment by matching references to exact definitions and details of data. Our team of experts works on the data sheet to structure the bulk of data as per your specific needs.

Data Preparation

A critical step in efficient prepping of data is Data Labeling, as it identifies the type of data being needed for the dataset. You can engage our team in creating additional examples or modifying existing ones to enhance the quality and diversity of the labelled dataset used for machine learning tasks. 

0 M

Items Moderated each day 

10 M
Live Streams each day
10 K
Profiles Reviewed each day
10 Y
of Experience
10 %
Availability

Empowering your business with Individualized solution

While a few platforms are tackling the issues related to content moderation, others are still in the process of determining their starting point. In contrast, we have already successfully implemented it. Experience our AI content moderation at its finest with ContentAnalyzer

With your dedicated account manager, as a single point of contact and accessible round the clock over the phone or messenger, you get a personalized support and swift communication literally in real time. We aim at seamless problem-solving, enhancing overall satisfaction on our service delivery and partnership effectiveness through continuous communication across multiple channels.

Content moderation for an app demands a tailor-made solution aligned with your project’s unique requirements. Our customized offerings ensure that the moderation process effectively aligns with your content types, user demographics and compliance mandates. We are your extended team working together towards user safety, platform integrity and user experience.
We understand that real-time implementation of moderation guideline changes in an app is crucial for maintaining user safety and adherence to evolving content standards. Swift updates prevent harmful or inappropriate content from slipping through the cracks, ensuring a responsive and adaptable moderation system that protects both users and the app’s reputation.

Applications and Capabilities

The most popular applications of this technology are for recognizing objects, faces, and action verbs in live situations. 

Applications

  • Online Security applications
  • AI Systems
  • Social Platforms
  • Audio and Video Applications
  • Ecommerce platforms

Capabilities

  • Recognizing real-world objects in digital data plays a large part in facilitating a secure software or web environment.
  • Capable to handle large data volumes
  • Multilingual team
  • Experienced Staffs for greater output

Speak with our subject matter experts

data-labeling

Data Labeling Services

What is Data Labeling?

Data labeling is the act of manually reviewing and placing relevant and useful labels on data. Data, in this case, can be any sort, including text, video, images, and audio. A data label, thus, is an identifying factor that describes what an item is. Data labeling has become an integral part of many work processes in businesses that use big data and artificial intelligence. Data labeling takes a series of unlabeled data items and augments each item with highly relevant labels based on categories. This makes possible the efficient extraction of useful information from a large amount of unstructured data. 

The challenge for most companies dealing with large amounts of unstructured data is how to make it manageable and meaningful for decision making. This calls for expertise in the area of Computer Vision and Audio Processing technologies, along with expertise in the areas of Databases, Information Life-cycle Management, Knowledge Warehouse Systems, and Data Labeling.

With the combined expertise of these, Foiwe can provide the necessary manpower that can work on your technical tools to effectively handle large data labeling tasks.

Related Services

Some of our offerings that can help you manage big data efficiently and enhance AI systems

Case Studies and Reports

Image moderation is the process of assessing, filtering and managing images, to ensure they adhere to specific guidelines, policies or standards. Image Moderation helps to remove the content that is offensive, illegal or against the platform’s terms of service.

  1. Nudity Detection: One of the most common types of image moderation is the detection of nudity or explicit content. Moderators check images for explicit body parts and flag or remove them accordingly to the online platform guidelines.
  2. Violence and Gore Detection: This type of image moderation, moderator identifies violent or gory images, preventing them from being shared on platforms that aim to maintain a non-violent environment.
  3. Hate Speech and Offensive Content: Image moderation can identify hate speech, offensive symbols and content that promotes discrimination, ensuring it doesn’t gain traction online.
  4. Copyright and Trademark Violations: In image moderation, moderators check for copyright and trademark violations, protecting intellectual property rights.
  5. Drugs and Substance Abuse: Images containing drug-related content can be flagged and removed to maintain a safe and responsible online space.
  6. Spam and Scams: Image moderation can help detect and filter out images associated with spam and scams. Also, using image moderation  protects users from online fraudulent activities.
  7. Brand Safety: It ensures that brands’ advertisements don’t appear next to inappropriate or controversial content, safeguarding their reputation.
  1. User Safety: Image moderation protects users, especially children and vulnerable individuals, from exposure to harmful and explicit content.
  2. Compliance with Regulations: Many countries have regulations in place that require online platforms to moderate and filter content. Image moderation also helps to follow rules of that country.
  3. Brand Reputation: For businesses and advertisers, image moderation is crucial to protecting their brand reputation by ensuring their ads don’t appear alongside offensive or inappropriate content.
  4. Community Guidelines: It helps maintain and enforce community guidelines on social media platforms and websites, fostering a respectful online environment.
  5. Content Quality: Image Moderation ensures that platforms maintain a high standard of content quality, making them more attractive to users and advertisers.

While image moderation is essential, it is not without challenges. Some of these challenges include:
False Positives and Negatives: Overly strict algorithms can result in false positives, flagging innocent content, while overly lenient ones may miss offensive material.
Evolving Content: As content creators find new ways to evade moderation, systems must continually adapt to emerging trends and techniques.
Scalability: For platforms with massive user bases, moderating images at scale can be a significant technical challenge.

Here are some key aspects of moderation in the media:

  1. Content Screening: Moderators review and assess user-generated content, such as text, images, videos and comments, to determine whether it violates platform-specific rules or standards. This may include identifying and removing content that is offensive, illegal, spammy or otherwise inappropriate.
  2. User Behavior Monitoring: Moderation extends beyond just content and may involve monitoring user behavior for violations. This can include identifying and taking action against users who engage in harassment, hate speech or other disruptive behaviors.
  3. Enforcement of Policies: Moderators enforce community guidelines, terms of service, and content policies set by the media platform. They may issue warnings, temporary suspensions, or permanent bans to users who repeatedly violate these policies.
  4. Filtering and Blocking: Automated tools and algorithms, in conjunction with human moderators, may be used to filter out or block certain keywords, phrases or types of content that are commonly associated with violations.
  5. Reporting Mechanisms: Media platforms often provide users with the ability to report content or users that they find objectionable. Moderators review these reports and take appropriate actions.
  6. Legal Compliance: In some cases, media moderation is necessary to ensure that content adheres to legal requirements, such as copyright laws, defamation laws or regulations related to hate speech and discrimination.
  7. Protecting Minors: On platforms frequented by minors, moderation may be particularly focused on preventing the dissemination of harmful or age-inappropriate content.
  8. User Engagement: Moderation can also play a role in fostering positive user engagement and community building by encouraging respectful and constructive interactions.
  9. Crisis Management: In the event of a crisis or emergency, moderators may be responsible for disseminating accurate information, preventing the spread of misinformation and managing communication channels effectively.
  10. Content Curation: Some media platforms employ content curators or editors who select and feature content to ensure it aligns with the platform’s objectives or editorial standards.
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