Practicus AI is a unified platform for generative AI and data intelligence. The company integrates data science, generative AI, MLOps, and DataOps into a single, 100% cloud-native platform designed to be robust, flexible, and accessible for a range of user needs. Practicus AI supports both code-based and no-code workflows, equipping organizations with tools to create and manage advanced AI and data-driven solutions.
The platform is built to help companies streamline the development and deployment of machine learning and AI applications, whether teams are experimenting with new models or running production workloads. Practicus AI’s focus on unifying disparate AI and data operations sets it apart, aiming to simplify the complexity of working across multiple AI, data, and operational pipelines.
Who Uses Practicus AI?
Practicus AI serves a broad range of business customers who require advanced data and AI capabilities. Notable organizations using Practicus AI include:
- Canva
- Boeing
- Albertsons
- Lowe's
- Trident
- Weyerhaeuser
These clients represent sectors from retail and manufacturing to technology and aviation, illustrating the platform’s versatility in meeting diverse industry needs.
How Was Practicus AI Started?
Practicus AI was founded by a team with extensive backgrounds in software engineering, artificial intelligence, and business development:
- Hakan Eren, with over 25 years in the software and AI industry, previously led AI/ML business units at AWS and Microsoft.
- Rohat Kaya brings nearly 20 years of experience in building and managing large software development teams.
- Nese Bilgen Canan, with more than 15 years in sales and business development, has led data, analytics, and machine learning initiatives at various systems integrators.
The leadership’s collective expertise shapes Practicus AI’s focus on delivering enterprise-grade AI and data intelligence solutions. More details about the team and company background can be found on their About page.
What Makes Practicus AI Different?
Practicus AI’s main differentiation lies in its unified approach: it combines generative AI, data science, MLOps, and DataOps within a single, cloud-native environment. This enables organizations to transition seamlessly between experimentation, development, and deployment—regardless of technical skill level. The flexibility to use code or no-code interfaces allows a wider range of users to participate in AI-driven innovation.
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