Dependable
and
High-performance
Technologies



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At Dependable Intelligence, we believe that artificial intelligence and machine learning should not only help people solve problems effectively and efficiently, but also provide solutions that are interpretable and trustworthy.

We provide advanced software and professional services for high-performance machine learning applications that require a high degree of reliability. We specialise in research and development of novel machine learning techniques that produce auditable predictive models which can be logically explained.

Sports Analytics

One of the major applications of Depintel's technologies is sports analytics. Working with top researchers at National University of Singapore and Griffith University, we have developed an AI that can analyse sports videos and provide insightful and in-depth stretagy and tactics recommendations using a combination of deep learning and probabilistic reasoning.

Read more:
Zhaoyu Liu, Kan Jiang, Zhe Hou, Yun Lin, and Jin Song Dong. Insight Analysis for Tennis Strategy and Tactics. IEEE International Conference on Data Mining 2023. PDF

In this research we have developed world-leading algorithms for analysing actions and events from sports videos. For example:

Tennis Ball Tracking


Tennis Action Detection & Court Mapping


Soccer Ball Tracking


Soccer Movement Detection & Court Mapping


We also work closely with OddsAI and major bookmakers in the world on soccer referee and VAR decision-making analysis.

News Room

Depintel is working closely with univerisites around the world to host the International Sports Analytics Conference and Exhibition (ISACE) series 2025 in Shanghai, China, on in September 2025.

Our eXplainable AI (XAI) tool DeepDebugger is released to help the user visualise and understand deep learning models and improve their performance.

Silas v0.8.7 is released with better machine learning performance, higher computational efficiency, eXplainable AI capabilities, and more.

N-PAT is released as an extension of the Process Analysis Toolkit that can perform (probabilistic) model checking on nested and hierarchical systems with parallelism.

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