A trusted data foundation that operationalizes enterprise data for production-grade AI systems, reliable analytics & natural language access.
Astera today announced the launch of Centerprise AI, the agentic evolution of its enterprise data management platform. Centerprise AI embeds a proprietary agentic harness across the data management stack, enabling data teams to design, test, and deploy data assets, warehouses, pipelines, models, and analytics in a single platform.
Centerprise AI supports structured sources such as databases, ERPs, and CRMs, as well as unstructured sources including documents, PDFs, presentations, images, videos, websites, portals, emails, and messages. It makes high-value business information accessible to people and AI systems through natural language, allowing data that was once difficult to extract, structure, label, and model to be operationalized at scale.
“AI has fundamentally expanded the universe of enterprise data that can be operationalized,” said Ibrahim Surani, Founder and CEO of Astera. “The idea behind Centerprise AI is simple: data at your fingertips.”
Centerprise AI: The Enterprise Data Foundation for Trusted Analytics and AI
Centerprise AI embeds an AI harness across Astera Centerprise, Astera’s data integration and management platform first released in 2011. Teams can describe what they need through a document or conversation, and the agent helps generate the data models, pipelines, warehouse infrastructure, integration flows, and migration workflows required.
Why Teams Can Trust Centerprise AI
Centerprise AI is built to give teams the speed and flexibility of AI without losing control, governance, or reliability.
- Conversational at design time, so teams can define what they need in plain language
- Deterministic at runtime, so deployed workflows run consistently on approved metadata, schemas, and logic
- Grounded in your business through AI Skills, so agents apply your logic, KPIs, domain terms, and analytical scenarios at runtime
- Built for data control, with support for locally hosted models to address data residency needs
Every Part of the Data Stack is Now Agentic
Centerprise AI brings agentic capabilities across the data stack. In data warehousing, an agent reads requirements, profiles source systems, designs staging and dimensional models, and builds the pipelines that populate the warehouse. In data modeling, teams can design, review, and refine models through conversation before forward-engineering them to the database.
In data integration and migration, agents map sources to targets, handle schema differences, manage transformation logic, and track workflows end to end. In data transformation, teams can build complex pipelines without manual scripting. In data prep and analytics, users can ask questions in plain language, build dashboards, and get governed answers from structured and unstructured sources.
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