Database Context for AI Applications

Database context, on demand for your AI applications

Give your AI apps existing schemas, SQL, report definitions, the code behind those reports, business semantics, and other database work as relevant context through MCP—automatically chunked for scale and kept current as your databases change.

LangGrant Automated Context Engineering for Databases connects existing databases, schemas, SQL, report definitions, report code, and business semantics to AI applications and agents through chunking, caching, scale, and current context.
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Supported databases

  • Snowflake
  • SQL Server
  • Oracle
  • PostgreSQL
  • BigQuery
  • Redshift
  • Azure SQL
  • Amazon Aurora & RDS
  • Databricks
  • Hive
  • MySQL
  • Salesforce

For AI developers

Database context for autonomous agents and AI apps

LangGrant supplies autonomous AI agents and applications with compact, current and relevant database context across large schemas, multiple databases and high request volumes.

Intelligent selection and chunking avoids dumping an entire schema into the prompt. LangGrant detects schema changes automatically, caches context and keeps it current for the next request.

Large schemas and high volumeMultiple databasesSchema change detectionContext cachingSQL, diffs and upgrade scriptsAnalytics and app development

An AI interoperability platform connecting models, agents and applications to the database context they need.