Gen AI

Context Layer for Your Agents

Give AI agents the governed context they need. Blindata exposes your metadata graph (ontology, catalog, lineage, and quality) as a structured context layer for external assistants and automation.

Overview

AI agents are only as reliable as the context they operate on. Dumping entire schemas or ontologies into a prompt is slow, expensive, and often misleading. What agents need instead is a governed context layer: a navigable map of your enterprise metadata that they can query on demand, pulling only what is relevant at each step of a reasoning plan.

Blindata is that layer. Your Business Glossary, Data Catalog, data products, lineage, and quality scores are connected in a metadata graph. External AI clients in tools like Cursor, Claude Desktop, or VS Code connect through a standardized protocol and access this graph via specialized tools: search, traverse relationships, fetch resource details, and more. The agent starts with minimal context and expands it surgically, grounded in definitions, ownership, and policies your organization already trusts.

Why a context layer matters

From guesswork to governed reasoning

Agents navigate the ontology first, then find certified data products, then fetch physical tables and columns, the same top-down path a skilled data engineer would follow.

Lean, focused context

Instead of brute-force context dumps, agents pull only the nodes and relationships they need. Faster responses, lower cost, and more accurate outcomes.

Trust built in

Results respect access controls and governance policies. Sensitive fields can be masked before exposure to external LLMs, according to your organization’s rules.

Works where you work

Connect your preferred AI environment to Blindata. The context layer meets agents in the IDE or assistant they already use, so there is no need to switch tools for every governance question.

Features

A typical agent workflow shows how the context layer operates in practice. A user asks: “Create a stored procedure that aggregates customer orders by region using reliable data.” A connected agent can:

Search the Business Glossary semantically to understand what “customer orders” means in your organization, not just match a keyword.

Follow relationships from business terms to certified data products with quality scores, ownership, and official designation as master sources.

Fetch the actual tables, columns, and types behind those products: verified names, not hallucinated schema.

Generate SQL, reports, or stewardship updates grounded in metadata your organization has already approved.

This is context engineering in practice: the discipline of grounding AI in the reality of your data. Read our deep dive in Data Governance and AI Context Engineering.

Three ways to use Gen AI in Blindata

Blindata AI Assistant Semantic Search Context Layer
Who Stewards & business users in Blindata Everyone discovering data Developers & power users with external AI tools
Where In-app chat (sparkle icon) Search across the platform Cursor, Claude, VS Code, and other MCP clients
Best for Day-to-day governance in plain language Finding assets by meaning or keyword Agent automation, code generation, advanced workflows

All three draw on the same governed metadata. Choose the entry point that fits how you work, or use them together.

For connection setup and tool reference, see the MCP Server documentation in the Help Center. For REST integrations and webhooks, explore Open API & MCP Server.