Awery Aviation Software has launched the beta version of Ask Awery, a new feature that allows customers to safely connect data from the company’s ERP platform to the leading AI tools, such as ChatGPT, Claude, Gemini and Cursor.
The functionality relies on the Model Context Protocol (MCP), a standard for AI applications to communicate with external systems and data sources. With the integration, users of Awery’s Enterprise Resource Planning (ERP) platform can query their operational information in the AI environment of their choice and receive the answers right in that tool.
The feature is meant to make operational data more easily accessible and to allow customers to embed AI into existing workflows, Awery said.
“Successful digital adoption in air cargo is about making the technology simple and usable for those who will use it on a daily basis, and this tool will enable our customers to benefit from it,” said Vitaly Smilianets, Founder and Chief Executive Officer of Awery.
Ask Awery allows users to ask their preferred AI engine to retrieve and combine data from their Awery accounts. Examples include asking for customer tonnage by lane or looking for charter quote conversion over a time period.
Tasks that used to take hours to manually piece together information from several different sources can now be done in seconds, the company says. Intended to provide customers with more time to analyze results and make informed decisions, to reduce the use of time and resources and ultimately, to improve service quality.
Ask Awery is fully integrated with Awery ERP to combine operational, commercial and financial processes into one platform.
Security and control of access are still tied to the current ERP environment. The AI integration is read-only and uses the user’s existing Awery rights to set permissions. So users will only ever see information they are already authorized to see.
Awery is also looking at going beyond the initial querying capability. Smilianets thinks AI agents could be used to do recurring reports and routine data checking. Ultimately customers could schedule agents to generate reports on a daily, weekly or monthly basis, removing repetitive tasks and freeing up teams to focus more time on data-driven decision-making.
