Viking Analytics
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Viking Analytics

http://vikinganalytics.se/
Last activity: 01.03.2024
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Categories: AnalyticsArtificial IntelligenceCauseDataHealthTechIndustryOptimizeProductSoftwareTools
Founded in 2017 in Sweden, Viking Analytics enables companies and their domain specialists with AI-based predictive maintenance applications for various industrial assets.

Our analytics software, MultiViz, allows industrial specialists to extract insights and value from both process data and asset data. They can perform data analysis, data annotations, build and deploy AI applications for different machines and processes.

MultiViz is currently available in two applications: vibration condition monitoring and valve condition monitoring.
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Location: Sweden, Gothenburg
Employees: 11-50
Total raised: $3.27M
Founded date: 2017

Investors 1

DateNameWebsite
16.02.2021MobilityXl...mobilityxl...

Funding Rounds 1

DateSeriesAmountInvestors
26.01.2023Series A$3.27M-

Mentions in press and media 4

DateTitleDescription
26.01.2023Viking Analytics raises Series A of more than € 3mn for global expansionViking Analytics raises Series A of more than € 3mn for global expansion Thu, Jan 26, 2023 14:30 CET Report this content Sweden-based Viking Analytics closed its Series A funding round with the support of Finindus, Industrifonden and ABB. T...
26.01.2023Provider of predictive maintenance Viking Analytics closes €3 million series AViking Analytics, an AI in predictive analytics startup from Gothenburg, Sweden, has closed its series A round at €3 million with the intent of expanding overseas. The deal was co-led by the Swedish investor Industrifonden and Belgium-based...
26.01.2023Viking Analytics Raises Over €3M In Series A FundingViking Analytics AB, a Gothenburg, Sweden based company developing AI-based machine health monitoring technology, closed its Series A round raising over €3m. The round was co-led by Finindus (backed by ArcelorMittal), and Industrifonden wit...
-Viking Analytics AB“Viking Analytics offers AI for vibration analysis so analysts can spend less time looking at data and more time on diagnostics and recommendations.”

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