Appsilon | R Shiny | Machine Learning

Updated in September, 2025

Appsilon is a leading provider of data-driven solutions, specializing in R Shiny applications and machine learning. With a strong focus on data visualization and interactive web applications, Appsilon empowers organizations to harness the power of data for informed decision-making. The company boasts extensive expertise across various industries, including healthcare, finance, and technology, ensuring tailored solutions that meet diverse client needs. With a global presence, Appsilon serves clients around the world, combining deep technical knowledge with a commitment to innovation. Key differentiators include their emphasis on user-centric design, collaborative development processes, and a robust portfolio of successful projects that demonstrate their ability to deliver impactful results. Through a blend of cutting-edge technology and strategic insights, Appsilon transforms complex data into actionable intelligence.

Overview

Company size

51-200 employees

Founded

2013

Hourly rate

$101 - $150 / hr

Minimal project

$10,000+

Headquarters

Warszawa, PL

Ready to start

1-2 weeks

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Appsilon | R Shiny | Machine Learning

Warszawa, Poland

Appsilon | R Shiny | Machine Learning provided

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          Top mentions

          Not for everyone, and here's why

          Expertise in R Shiny

          Appsilon is highly regarded for its expertise in R Shiny applications, providing robust solutions tailored to data visualization needs. Clients appreciate the depth of knowledge they bring to projects.

          Higher Cost

          Some clients have pointed out that Appsilon's services can be on the pricier side compared to other IT service providers, which may be a concern for budget-conscious organizations.

          Customer sentiment at a glance

          Appsilon offers numerous advantages, including expertise in R Shiny and machine learning, high-quality deliverables, and a collaborative approach. However, clients should consider potential disadvantages like higher costs and limited scalability for larger projects.

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