Astra Group Releases Low-Code Astra AI Platform for Building Corporate AI Agents in Closed Environments

Astra Group has launched the Astra AI Platform, a low-code constructor designed for creating corporate AI applications that operate entirely within closed circuits without reliance on foreign clouds. The platform targets small and medium businesses as well as large corporations, enabling rapid deployment of AI secretaries, technical support assistants, and other agents in days rather than months. It forms part of the broader Astra AI ecosystem and includes two main constructors: one for machine learning model training and deployment, and another for LLM-based language assistants, document search, and process automation. Users can assemble scenarios from visual blocks, debug them in real time, or extend functionality with custom Python code when needed. The solution supports RAG-based knowledge bases, integration with external tools such as CRM and ERP systems, and the coordination of multiple agents for distributed task handling. One demonstrated use case involves processing ten pages of engineering drawings and specifications in five minutes to prepare data for downstream systems. The product is already listed in the Russian domestic software registry and available to customers.

AntiMalwareTranslated from Russian

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Astra Group has introduced the Astra AI Platform, a low-code constructor for corporate AI applications that can be deployed locally in closed environments without dependence on foreign cloud services.

The solution is aimed at small and medium businesses as well as large corporations and is positioned within the company’s existing ecosystem for secure development processes, including ASOC and DevSecOps practices.

According to the developer, the platform reduces implementation time from several months to just a few days. It consists of two integrated constructors: an ML module responsible for training and deploying machine learning models, and an LLM module focused on language assistants, document-based search, and workflow automation.

Applications are built from visual blocks that can be configured and debugged in real time. When visual tools are insufficient, developers may edit the underlying code or add custom blocks written in Python.

The platform supports enterprise knowledge bases for RAG retrieval, allowing agents to search internal company documents. External tool connections enable integration with CRM, ERP, email systems, and web search services.

Multiple agents can be combined and assigned different tasks. Example scenarios include analytics, financial operations, technical support, and risk monitoring.

In one company-provided demonstration, the models processed ten pages of engineering drawings and specifications within five minutes, preparing structured data for downstream engineering systems.

The product is registered in the Russian domestic software registry and is now available to customers.

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