Applying LLMs to deliver answers to business questions requires LLM generated plans that are documented, easily explained and validated, and ready for execution.
AI transparency and explainability
Documented LLM generated plans that are transparent and explainable, help ensure quality needed for trust within the enterprise, and compliance to enterprise and regulatory requirements and risk management.
LangGrant delivers transparency and explainable results. LLM plans are documented and saved, for human review and validation.
Lifecycle management and data delivery
Each generated plan is attached to a query, saved, and can be executed to deliver data. When needed, plans become part of a daily or real-time production workflow, with data delivered as a flat file, a database table, or to a data lake.
Automated database context
LangGrant automatically delivers complete database context for LLMs to comprehend multiple databases simultaneously at scale. Like a skilled engineer, once an LLM understands databases it can contribute to solution design.
Micro data lakes on demand
LangGrant binds LLMs to create accurate analytic plans for user queries, resulting in a inference ready “micro data lake.” Plans are saved, easily validated and modified, and run to deliver the analytic data within minutes of the user query.
Governance
PII safeguards, authorization controls, data residency rules, firewall restrictions, and token-governance policies are built-in by design. No sensitive data leaves governed systems.
Plan management
LLM generated plans are saved, easily reviewed and validated, modified, and executed, for LLM use that is transparent, explainable, and repeatable.
Database cloning and containers
On demand database clones with containers provide Agent developers with production database copies (with optional masking) for agentic AI dev/test.
Database subsetting and synthetic data
Database subsetting with synthetic data provides added context for working with complex multi-database environments.