DataRobot, the provider of AI that makes business sense, announced an enterprise AI suite to develop and deliver generative AI applications and agents that can be customized to meet business needs.

Despite increased investments in AI, only 34% of AI professionals feel fully equipped to meet business goals, according to a recent DataRobot survey. Over half of survey respondents cited the need for best practices and out-of-the-box approaches for developing and deploying AI solutions.

“It’s time for a new approach to AI focused on business outcomes,” said Debanjan Saha, Chief Executive Officer, DataRobot. “The DataRobot enterprise AI suite answers the need for advanced AI solutions for today’s businesses. From personalized content generation to more complex business workflows, DataRobot AI applications and agents are built with business logic, security guardrails, and a seamless user experience to deliver value in 30 days.”

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The new DataRobot enterprise AI suite maximizes impact for your business by enabling teams to:

Deliver advanced generative AI solutions.

  • Composable AI applications and agents: Customize pre-built application templates for a wide range of AI use cases, including agentic workflows, data analysis tools, and predictive content creation systems, with the ability to tailor security, business, and implementation logic.
  • Collaborative AI application library: Enable users and teams across the business to work together from a common repository, whether they’re creating new applications or enhancing existing ones.

Rapidly prototype and perfect the user experience for generative AI applications. 

  • Generative AI application workshop: Facilitate rapid prototyping and seamless production deployment with automated monitoring and scaling. Developers can build custom generative AI application interfaces with out-of-the-box examples for Streamlit, Flask, and Slack, or create bespoke interfaces with a preferred framework such as Dash, Shiny, Flask, and Microsoft Teams. Application experiences are tuned, refined, and viewed in real time, streamlining how teams push to production and minimize downtime.
  • Out-of-the-box Q&A application: Stress test generative AI applications through facilitated red team activities pre-production, ensuring they meet business needs and requirements.

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Streamline application publishing.

  • Modern developer building blocks: New declarative API framework makes it easy to replicate work, visualize, and save AI pipelines to develop end-to-end generative AI applications faster.
  • Instant application updates: Simplify maintenance by instantly pushing updates, fixes, and improvements to applications without user downtime.
  • One-click integration to the SAP ecosystem: With DataRobot’s optimized SAP Datasphere connector and SAP AI Core one-click deployment, applications are integrated into existing SAP infrastructure quickly and easily.

DataRobot AI applications are already delivering value for customers, partners and industry experts:

JG Chirapurath, Chief Marketing Officer, SAP BTP: “Our partnership with DataRobot is a critical component in delivering AI value to customers from their reliable and trusted SAP data. The new DataRobot applications and integrations with SAP will help simplify and speed-up delivery of advanced use cases that provide mission-critical value to all our customers.”

Amit Jain, Chief Technology Officer, Personify Health: “Getting from idea to model implementation has always been a pain point for data scientists. DataRobot AI applications have dramatically shifted the curve for us by reducing the time from months to hours. That’s a huge win and a fundamental change in how we productionalize models.”

Ritu Jyoti, General Manager/Group VP, AI and Data Market Research and Advisory, IDC: “The ability to rapidly prototype and deploy generative AI applications is becoming a critical differentiator for businesses. DataRobot provides developers with the framework and pre-built components needed to bring innovative generative AI solutions to market quickly. The open architecture ensures that AI teams aren’t locked in or stagnating.”

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