Multimodal Dataset for Analytics and AI Training

Traversals Data

Multimodal Dataset for Analytics and AI Training

Traversals developed a Multimodal Dataset for Analytics and AI Training [MDAA], providing clients with the ability to perform trend analytics and train custom AI models on historical data. This dataset contains a diverse range of data types, including images, text, and video, enabling clients to train models that can handle multimodal inputs. By leveraging this dataset, clients can gain valuable insights into their business operations and extract meaningful information from their data. Additionally, clients can use this dataset to develop custom AI models that meet their specific needs and business requirements.



In today’s fast-paced business environment, data is becoming increasingly important. Companies collect or generate vast amounts of data from various sources, including customer interactions, transactions, and operations. Analyzing this data and extracting meaningful insights is critical for businesses to make informed decisions and gain a competitive advantage. Artificial intelligence (AI) is one of the most powerful tools available for analyzing data and deriving valuable insights. However, to train AI models effectively, businesses need access to a diverse range of data types, which is where a multimodal dataset comes into play.


A multimodal dataset is a collection of data that includes various types of data, such as images, text and video. By leveraging a multimodal dataset, businesses can train AI models that can handle multiple types of data inputs, making them more versatile and effective. For example, a multimodal dataset can help a company train a model that can analyze customer feedback in various formats, including text, audio, and video. This enables the company to gain a more comprehensive understanding of their customers’ needs and preferences, leading to more effective product development and marketing strategies.


Furthermore, a multimodal dataset can help governmental organizations to gain insights into their operations by analyzing data from public and proprietary sources.


In summary, a multimodal dataset is an essential resource for organizations seeking to derive actionable insights from their data and build custom AI models. By leveraging a diverse range of data types, organizations can train more effective models that can handle various inputs, leading to more informed decision-making, better operational efficiency, and a competitive advantage.


Core Features

[MDAA] allows the generation of data sets with historical and multimodal information for long-term analysis.

[MDAA] provides realistic images and videos from different perspectives (bird and first-person) to train AI models.

[MDAA] provides images and videos for the training of object detection, identification and tracking.

[MDAA] provides images and videos to train AI models for logo/patch identification.

[MDAA] provides multilingual text to train AI models for Natural Language Processing, such as entity recognition, sentiment analysis, event classification, geocoding, …

[UDFM] searches multilingual sources for the latest news in real-time around the clock and provides the information found after AI-based pre-processing.

[UDFM] integrates seamlessly with proprietary solutions. Traversals provides expertise and support as needed.

Multimodal Dataset for Analytics and AI Training


Technical Details

Fast, secure and always up to date. The [MDAA] service is constantly being improved through customer feedback. New features are implemented in the shortest possible time thanks to the modern DevSecOps architecture and improve customer deployment.

Video dataset containing more than 400,000 videos of various length and quality from curated data sources.

Image dataset containing more than 700,000 images of various sizes and quality from curated data sources.

Text dataset containing more than 4,000,000 text messages of various sizes from curated data sources.

Various levels of optional data enrichment starting with raw data to hand-labeled data.

Optional support to set up a full MLOps pipeline with annotation tools, AI training, version control for AI modes, …

SaaS hosting in the EU cloud with web client or API (Application Programming Interface).



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