Problem solving in the age of AI

The field of Machine Learning and Artificial Intelligence is evolving into a big multi-functioning model that has expertise to do plenty of things and solve many complex problems.

With a decade of experience in Labeling and Annotation, creating sophisticated training data sets for Machine Learning / AI Algorithms, and enabling machines to learn, GlobalLogic partners with global technology companies to design and build the AI-enabled world. GlobalLogic provides advisory, ideation and implementation services in Labeling and Annotation to companies across verticals, globally.

Machine Learning Methods

We partner and help our clients understand and interpret images, video, sounds, voice, text and all forms of unstructured data to get actionable insights.


Supervised Learning

Historical data predicts likely future events, via methods such as classification, regression, prediction and gradient boosting.


Semi Supervised Learning

The cost associated with labeling is too high to allow for a fully labeled training process.


Unsupervised Learning

The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data.


Reinforcement Learning

Used for robotics, gaming and navigation. The algorithm discovers through trial and error which actions yield the greatest rewards.

Who can adapt to Machine Learning?

Most industries working with large amounts of data have recognized the value of Machine Learning technology and are able to work more efficiently.








Financial Services









About GlobalLogic

Rooted in Silicon Valley, GlobalLogic operates design studios and engineering centers in 14 countries across 4 continents, extending our deep expertise in strategy & design, DevOps, big data & analytics, AI/ML, etc. We partner with global technology companies to design and build next-gen geospatial solutions that serve their business needs.

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    Work Examples

    Content classification

    Creating Rich Shopping Experience with Content Classification

    The customer is one of the most popular and advanced technology companies in the world. They partnered with GlobalLogic in 2013 to streamline their e-Commerce business and improve the experience for the end users. The end customers are constantly shopping and comparing prices online between different vendors. The customer’s mission was to make every information organized and make it universally accessible and useful with a huge focus on innovation, retention and user experience.

    Product title curation

    Search enablement for Visually challenged shoppers

    The customer is an American multinational technology company that specializes in Internet-related services and products. They partnered with GlobalLogic in 2018 with an aim to cater to and improve the end-user experience of all kinds of audiences in the US shopping domain. The customer’s mission was to recurate product titles for an accurate and quicker response.

    Content filtering & mapping

    Content Filtering & Mapping for Desired Search Results

    This Silicon valley technology giant is one of the most popular search engine organizations around the globe. They partnered with GlobalLogic in 2018 to ensure the sanctity, precision, suitability and compliance nature of the online content.

    Annotation for sports analytics

    Annotation for Sports Analytics

    We provide meaningful context and a frame of reference to Machine Learning Models through Annotation and Labeling - by turning live broadcast feed into training datasets to be fed into smart Sports Analytics algorithms. GlobalLogic annotators analyze sports event videos and record and label key events as they occur. This provides detailed, labeled information which can then be used for advanced analytics on games like Hockey and Football, changing the way teams analyze performance and recruit players.

    Next gen solutions for autonomous vehicles min

    Next-gen solutions for autonomous vehicles

    The client is one of the leading technology companies that conceptualizes and develops next-generation solutions for autonomous vehicles. Their objective was to improve and enhance the functionalities of self-driving cars. This was made possible by integrating Machine Learning technology into autonomous cars enabling them to better sense the environment around them improving safety and reliability, requiring very little or no human intervention.

    Damage detector model for car manufacturing companies min

    Damage Detector Model for car manufacturing companies

    The client is one of the largest financial services companies in the world. Their objective was to develop world class AI & ML models for their various products for which machines are required to be trained using high quality and unbiased datasets achieved through human evaluation process.

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