An AI assistant using proprietary unsupervised algorithm(s) to automatically analyse large volumes of complex data to detect anomalies and recognize patterns in real time

Grant number: 960719

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Key facts

  • Disease

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    EC (Horizon)
  • Principle Investigator

  • Research Location

    Germany, Europe
  • Lead Research Institution

  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory


  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details


  • Broad Policy Alignment


  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract is an AI anomaly detection tool for large volumes of time series data in real time. Its unsupervised machine learning enables identification of "new anomalies" without the need for annotated data. Our unique selling point is that identifies outliers and unusual behaviour using algorithms that are selected through our proprietary benchmarking algorithm. As a result, our users can benefit from an up to 85% reduction in time spent on data analysis, analysis of 100% of the available data and improved detection rate of previously unknown anomalies. The gap between the rate at which data is being collected and how fast it can be analysed is increasing exponentially, contributing to business insight latency amongst analysts and engineers in sectors as varied as manufacturing to healthcare. E.g. in the automotive industry, anomalies that go undetected can result in defective components going unnoticed until after market launch. In 2016 alone, defective components cost the car industry a record $22 billion through car recalls. The market size for testing in the automotive sector is now expected to grow at a CAGR of 5.02% from €15.9B in 2017 to €24.3B in 2023. Given the COVID-19 pandemic that the world is facing,'s core technology can aid with more quality and efficient virus testing by monitoring diagnostic machines and analysing test results to identify patterns for research. One of our successfully completed pilot projects was with Roche diagnostics, the project involved monitoring the Analyzer machine data. This project is being undertaken by GmbH based in Berlin. The CEO & one of the co-founders previously founded Plastelina which peaked 1.5m unique monthly visitors in 2000 representing 0.6% of all internet users then. The team has 12 employees of which 4 have PhDs. Through the project the company expects to create 18 new jobs and generate cumulative first 5 year profits of €18.84 million with a ROI of 493%.