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Aiforia provides software and services for pathologists, scientists and healthcare professionals to use artificial intelligence for image analysis.
Seeing a gap in the marketplace, we sought to simplify complex operations for higher education. Knowing that institutions have major concerns regarding compliance adherence, and are plagued with paper, antiquated systems, difficult workflows, rising costs, and changing education models—we knew it had to make a difference in helping schools and students get enrolled, financed, and employed. The inspiration to create a SaaS-based technology solution came from the realization that there were no other options available in the marketplace, especially in the areas of enrollment and student finance. Thus, Education Partners was born and the mission to simplify the complex began. At Education Partners, we intend to simplify and standardize complex operations while removing risk one department at a time. Our SaaS-based technology solution addresses enrollment—in particular—admissions and financial aid as they are two of the areas that tend to be document, compliance, and process intensive. In short, we are committed to provide SaaS-based technology solutions that fundamentally change the relationships, experiences, and ultimately outcomes between students, schools, and employers.
Plains.net Internet Service is a Yuma, CO-based company in the Software and Internet sector.
eDreamz Technologies Pvt. is a Beverly, MA-based company in the Software and Internet sector.
Imagine what you can do when you know how much of your initiatives contributes to your outcomes. Causal Machine Learning (CML) algorithms recommend how you can innovate your practices to achieve greater business outcomes. CML Insight develops solutions that fulfill this vision. Our development teams are based in the US and Sri Lanka and our team members have years of experience in Machine Learning in general and Causal Machine Learning in particular. We work with clients in Higher Ed, Healthcare and Fintech. Our solutions are already proving the value of Causal Machine Learning in these domains.