LangGrant LEDGE: LLM enterprise database (orchestration) governance engine
Database modernisation and synthetic data company LangGrant has launched LEDGE MCP server.
This technology enables LLMs to reason across multiple databases at scale, execute multi-step analytics plans and accelerate agentic AI development without sending data to the LLM or breaching governed boundaries.
“The LEDGE MCP Server removes the friction between LLMs and enterprise data,” said Ramesh Parameswaran, LangGrants’ CEO, CTO and co-founder. “With this release, enterprises can apply agentic AI directly to existing database environments like Oracle, SQL Server, Postgres, Snowflake – securely, cost-effectively, and with full human oversight.”
Parameswaran says that “context engineering” is emerging as a foundational discipline in the AI era, especially as agentic systems and LLM-driven automation move from demos to production.
However, he says, several technical challenges must be addressed to fully unlock its potential in existing enterprise data assets within a modern AI architecture.
Five persistent barriers
Enterprises have rapidly adopted LLMs and AI assistants, but Parameswaran and team say they face five persistent barriers when applying them to operational databases:
- Security and governance policies block LLM adoption – Since most enterprises cannot permit direct access or data movement outside governed systems, the use of LLMs can be limited.
- Token and compute costs escalate as organisations push raw data (sometimes millions of rows) into LLMs for analysis.
- Agent developers need production-like data – this is required to build and test models – but they lack a safe, on-demand way to clone complex enterprise databases.
- Databases are not designed for LLM consumption. They are massive, complex, and unintuitive. Business users frequently need to join tables, but even LLMs with extended context windows struggle to handle that scale or maintain accuracy.
- Software engineers are only doing manual context engineering. Writing queries and data pipelines with tools like Co-Pilot, is a very manual process wherein they are providing context in bits & pieces to the LLM, which takes weeks.
These challenges are addressed through foundational capabilities.
Core functions
LEDGE orchestrates LLMs to deliver results while still complying with enterprise data policies. Analytics and reasoning occur using metadata and schema context – no raw data or large payloads are transmitted to the LLM. This dramatically lowers token costs, eliminates API-billing friction, and enables practical scale for enterprise agentic AI.
The technology also offers multi-step analytics plans i.e. LEDGE MCP automates query planning and orchestration, generating precise, multi-stage analytics workflows autonomously, while remaining reviewable and auditable by human teams.
Agent developers can instantly provision production-like, isolated clones and containers for developing, testing and tuning AI agents all without impacting live databases or creating uncontrolled copies.
LLMs can now comprehend and reason across multiple heterogeneous databases. The LEDGE MCP Server maps schemas, relationships and metadata, letting LLMs “see” the entire data landscape without reading the underlying data.