Data Annotation Services

Business benefits of AI data annotation

Improves the accuracy of outputs

The higher the quality of data used to train the AI model, the better outcomes we can get. Data annotation provides the AI algorithms with the knowledge to enable them to get their job done, which makes this “prework” essential for their proper usage. Without data labeling, AI just can’t understand what it is supposed to do with the data or twist the results, misleading users.

Better user experience

High-performative AI models trained on high-quality data sets provide end-users with an enhanced, frictionless experience. Chatbots or virtual assistants are able to assist users immediately by answering questions about a particular product or service or giving them general information about the weather conditions or news etc.

AI models scalability

Properly annotated data accelerate the process of training AI models and enable data engineers to scale them for diverse datasets of any volume.

Types of data annotation

Text annotation

​​Text annotation is the process that relies on adding metadata or labels to text data, such as natural language sentences or documents, to indicate specific qualities or elements of the text, such as named entities, sentiment analyses, or semantic segmentation (known also as semantic role labeling, semantic segmentation refers to teaching the machines to understand the underlying meaning and relationships between words in a sentence or document).

Text annotations are typically used to optimize chatbot services and search engine relevancy or – in general – to train natural language processing models (NLP models).

Image annotation & Video annotation

Within image annotation, we add pre-defined labels to images or videos to equip Computer Vision models with the capacity to decipher what’s displayed on the screen.

Image annotation and video annotation are widely used to train Machine Learning models, AI models, or Computer Vision models designed to block sensitive content in media, guide autonomous vehicles, quality control processes, or enhance security in objects.

Image annotation can be done manually or using automated tools, depending on the required quality and quantity of annotations.

Audio annotation

The process of labeling audio sound or speech recorded in any format in order to make it understandable for NLP-based. Audio annotation is used to train AI models in virtual voice assistants (Siri or Alexa) or voice chatbots.

Industries

Data Annotation Services delivers high-quality training data for Machine Learning and Artificial Intelligence models used in various industries.

AI Data Annotation in Retail

Data annotation in retail can be used in a number of applications in the retail industry, such as product categorization, customer behavior analysis, and personalized marketing.

AI Data Annotation in Finance

Data annotation enables the algorithms to accurately identify patterns and make predictions about financial trends and behaviors. They can be used for tasks such as credit scoring, fraud detection, and stock market analysis, among others.

AI Data Annotation in Customer Service

Data annotation in customer service can be used to label and categorize customer interactions, such as emails, chat transcripts, and phone calls, to enable the algorithms to understand the context and intent of the customer’s inquiries and identify patterns in customer behavior.

AI Data Annotation in Automotive

Data Annotation Services enable to build of applications for autonomous driving systems and predictive analysis. Annotated images and videos can be used to identify traffic signals or other external features, while sensor readings can be annotated to detect or force changes in vehicle performance.

AI Data Annotation in Healthcare

Annotated healthcare data can be used for a variety of purposes, such as disease diagnosis, patient monitoring, and drug discovery. Annotated medical images can be used to identify specific features such as tumors, while electronic health records can be annotated to extract important information such as patient history and medications.

The whole process step by step

1. Data collection
We extract data suitable for specific business problems and collect them.

2. Defining data classification
We establish data categories for suitable data classification.

3. Describing data classes
We create rules describing each we defined at the previous stage.

4. Data annotation
Using various manual and automated methods, we begin the data annotation process.

Addepto is a fast-paced, growing company focused on innovations in AI-related and data-oriented areas.

We support businesses worldwide in digital transformation, helping them find ways to use the data they have.

We always try new things and don’t shy away from out-of-the-box ways to develop the best solutions in Machine Learning, Business Intelligence, and Artificial Intelligence.