Artificial intelligence startup DataRobot Inc. today announced a suite of enterprise AI tools to help business customers build generative AI apps and agents customized to meet their needs.

With the new enterprise AI toolset, DataRobot customers will find everything they need to build and deploy AI apps faster using prebuilt application templates for a wide range of AI use cases, including agentic workflows, data analysis tools and content creation systems.

Venky Veeraraghavan, chief product officer of DataRobot, told SiliconANGLE in an interview this release represents a shift in the company’s AI vision from acting as a platform to providing enterprise application tooling. Currently, customers face major challenges in integrating AI into existing business workflows, addressing the complexity and reliability of AI and the needs of modern AI teams.

“For us to hit that market, we want to make sure there’s a way for the AI lifecycle to meet the dev lifecyle,” Veeraraghavan explained.

To make this possible, DataRobot provided a generative AI application workshop with out-of-the-box examples of for developers so that data could be easily streamed in from data science teams and sources. This means that AI models and sources could be connected easily to user interfaces for Streamlit, Flask and Slack or using bespoke interfaces with frameworks such as Dash and Shiny.

The enterprise AI suite also allows teams to use the company’s comprehensive stress testing of generative AI applications for quality assurance before pre-production to make sure they meet business requirements.

“The ability to rapidly prototype and deploy generative AI applications is becoming a critical differentiator for businesses,” said Ritu Jyoti, group vice president of AI and data market research and advisory at International Data Corp. “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.”

Additionally, the company announced add-on AI observability and compliance documentation for generative AI applications designed to help safeguard with real-time intervention and governance with minimal coding.

Compliance teams can automate AI compliance documentation that adheres to international, local and industry regulations with one-click for various models, including the EU AI Act and NYC Law No. 144. The same documentation can be used to stay compliant in real-time testing using guard libraries with alerts and intervention for models on OpenAI, Google LLC’s Vertex, Microsoft Corp.’s Azure and Databricks Inc.

“And for AI, if you can see the regulations, they deliver a bunch of controls,” said Veeraraghavan. “The controls become tests. Those tests are what’s run. You take the results and you document them. Now you can clearly express to the to the regulator what you did if you ever get asked. This is what we did.”

According to a recent DataRobot survey, 45% of AI professional respondents said they had difficulties with the reliability and consistency of their models. This included mature organizations, making it a top challenge, observability and data consistency monitoring in real-time was a significant concern.

DataRobot also announced large and unstructured data preparation handling automation tools to help businesses assess data quality, remediate issues and build accurate models at scale. The tools include the methods for building vector databases for advanced embeddings to improve accuracy for large documents, PDFs and scanned images with text.

Every part of the new enterprise AI suite, and DataRobot’s new AI vision, Veeraraghavan said, addresses a piece of the modern AI team and the challenges that the industry faces when adopting and building AI applications.

“I think that modern AI requires a hybrid team: software developers, data scientists and subject matter experts,” Veeraraghavan said. “Very broadly being able to have them all collaborate together and actually deliver an application using their own styles of tools is one of the hardest problems people have.”

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