Dataiku Cobuild is now generally available. It introduces an AI building agent designed to close the enterprise AI execution gap. Dataiku Cobuild enables organizations to turn plain language business objectives into governed, production-ready AI projects without writing code.

The launch comes at a critical moment for enterprise AI adoption. Over the past few years, organizations have invested heavily in modern data infrastructure and AI strategies. However, a persistent gap remains between experimentation and full production deployment. As a result, many AI initiatives stay stuck in backlogs or isolated prototypes.

Meanwhile, code generation tools have accelerated software development. Yet, their outputs often remain difficult for governance and business teams to inspect or control. In addition, standalone agent builders can produce prototypes that sit outside enterprise systems. Therefore, organizations face rising technical debt and slower AI operationalization.

Dataiku Cobuild addresses this challenge by converting business intent into structured AI workflows. It starts with a business problem and generates a complete Dataiku project. This includes data pipelines, machine learning models, AI agents, and applications. Importantly, all outputs are rendered as visual flows that stakeholders can review, edit, and approve before production.

Furthermore, governance is embedded from the start. Dataiku Cobuild works within existing enterprise permissioning and control frameworks. This ensures that AI development remains compliant and transparent across teams.

According to Neil Patel, Senior Director, Analytics Experience at Pfizer, AI-assisted building must go beyond speed. He noted that enterprise outputs must remain explainable, auditable, and safe for production use, especially in regulated industries such as pharma.

Similarly, Clément Stenac, co-founder and CTO of Dataiku, stated that AI-assisted development only delivers value when outputs can operate within enterprise constraints. He added that Cobuild balances AI speed with business input and IT governance.

In addition, Dataiku Cobuild supports flexible model ecosystems. Enterprises can use Dataiku AI Services or connect their own models through Dataiku LLM Mesh. It also integrates with platforms such as Snowflake Cortex AI, Databricks AI Gateway, AWS Bedrock, Google Gemini, Microsoft Foundry, OpenAI, and Anthropic. This allows organizations to maintain control over model choice, data residency, and compliance requirements.

Overall, Dataiku Cobuild aims to reduce friction between AI ideation and production deployment. It supports enterprises in scaling AI development while maintaining governance, transparency, and control. Ultimately, Dataiku Cobuild strengthens the link between business intent and operational AI delivery, making Dataiku Cobuild a key step toward enterprise-ready AI execution.

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