"LangGrant has launched the LEDGE MCP Server, a new enterprise platform designed to let large language models reason across complex database environments without directly accessing or exposing underlying data. The release aims to remove some of the biggest barriers organizations face when applying agentic AI to governed, production data, namely, security restrictions, runaway token costs, and unreliable analytics results. The company says the LEDGE MCP Server allows LLMs to generate accurate, executable multi-step analytics plans across databases such as Oracle, SQL Server, Postgres, and Snowflake, while keeping data fully within enterprise boundaries."
"By relying on schema, metadata, and relationships rather than raw records, the platform eliminates the need to push large datasets into LLMs, dramatically reducing token usage and preventing sensitive data leakage. According to LangGrant, tasks that typically take weeks of manual query writing and validation can now be completed in minutes with full human review and auditability."
""The LEDGE MCP Server removes the friction between LLMs and enterprise data," said Ramesh Parameswaran, CEO, CTO, and co-founder of LangGrant. He noted that enterprises can now apply agentic AI directly to existing database ecosystems securely and cost-effectively, without compromising governance or oversight."
LEDGE MCP Server enables large language models to reason across complex enterprise database environments without accessing raw records by using schema, metadata, and relationships. The platform supports databases such as Oracle, SQL Server, Postgres, and Snowflake while keeping data fully within enterprise boundaries. Eliminating the need to push large datasets into models reduces token usage and prevents sensitive data leakage. The system generates accurate, executable multi-step analytics plans and offers full human review and auditability, converting tasks that once took weeks of manual query writing and validation into processes that can complete in minutes.
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