Celigo enables business users to build and manage AI-driven workflows

Celigo Inc., a maker of integration platforms as a service, today introduced new capabilities aimed at simplifying how enterprises build and scale artificial intelligence-driven automation.

The Redwood City, California-based company announced Celigo Ora, a natural language interface, and a new Agent Builder tool that enables non-technical users to create and manage complex workflows across enterprise systems. The company said these additions are aimed at helping organizations move their AI efforts from pilot to production.

Celigo Ora allows users to design, modify, and troubleshoot integrations using conversational prompts instead of code. The interface leverages a network of specialized AI agents that understand business systems, workflows, and data context, allowing users to perform tasks such as building workflows and resolving errors without submitting an IT ticket.

Agent Builder complements Ora by providing a low-code environment for developing AI-driven automation that can infer tasks and operate across systems. These tools are integrated with Celigo’s Model Context Protocol server to provide secure connectivity between AI agents and enterprise applications.

The company says that by combining these products, organizations can manage automation scenarios ranging from rules-based processes to adaptive AI agents within a single platform, while maintaining IT-driven governance controls such as auditability and human-involved approvals.

Matt Graney, chief product officer at Celigo, said in a statement: “Any team can now write what they need to put into production, from simple integrations to autonomous agents, while IT maintains the control and governance the enterprise requires.”

This announcement reflects the broader challenges companies face as they attempt to operationalize AI. According to a Celigo-sponsored study conducted by MIT Technology Review Insights, only 1% of organizations without an integrated integration strategy are successful in scaling AI beyond a single department. In contrast, 90% of companies implementing production AI workflows are already using an integrated platform.

Celigo said this gap highlights the importance of the underlying infrastructure, beyond just model capabilities, in determining whether an AI initiative is successful at scale. Many organizations are still stuck in early stage pilots due to fragmented systems and a lack of standardized integration frameworks.

Celigo said it is addressing this issue by providing a standardized way to connect AI agents to enterprise systems while enforcing governance policies.

Celigo Ora is currently available in beta testing, while Agent Builder and MCP Server are generally available.

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