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IBS Staffing is a Oak Brook, IL-based company in the Software & Internet sector.
Our AI-first solutions bring the latest computer vision research and machine learning techniques to visual assessment. Using customer images, our AI products started enabling fast, accurate damage appraisal to help make recovery from accidents up to ten times faster. We`re now applying our award-winning AI to accelerate other processes involving visual inspection and appraisal. From salvaging car parts to property assessment, we`re on a mission to transform the way people live and work across whole industries. With AI on our side, we can free employee time for more valuable tasks, improve customer experiences, accelerate repairs and increase recycling – better for all of us, and the planet. Founded in 2014, Tractable has a world-class research and development team with over 30 years of combined research experience, and its solution is built on five years of dedicated research and development work undertaken by a team of Oxford/Cambridge-trained researchers. Tractable has raised $115m from Georgian Partners, Insight Partners and other top-tier investors. It achieved Unicorn status in 2021, is based in London, with other locations across the world.
GryphTech is a Etobicoke, ON-based company in the Software & Internet sector.
Split is the leading Feature Delivery and Experimentation Platform for enterprises that want to release features as fast as they can develop them confidently. Split`s fine-grained management, real-time monitoring, and data-driven experimentation ensure that new features improve the customer experience without breaking or degrading performance. Split ingests performance data and runs real-time statistical analyses on every new feature so that engineering teams can respond immediately to bad releases, measure changes in user experience, and build high-quality software. Engineering teams at Quicken Loans, Twilio, Salesforce, and WePay, a Chase company, trust Split to power their feature delivery.
At Molecula, we believe there is a tipping point in data access where the effort needed to enable real-time use cases and make big data ready for machine-scale analytics and AI, far exceeds the value created with the data.