Building Enterprise Chatbots and AI Assistants Grounded in Internal Company Knowledge

Build enterprise chatbots and AI assistants that are secure and grounded in internal company knowledge using Unity Catalog for data governance, Lakebase for operational state, and Agent Bricks for agent deployment, all hosted within Databricks Apps. This stack ensures data privacy and delivers context-aware, reliable responses.

Why This Stack Fits

Enterprises need reliable chatbots with strong data governance. Disconnected knowledge bases and weak governance compromise AI accuracy and data security. The Databricks stack directly addresses this need by:

Unity Catalog: Provides a unified governance and permission model across all data and AI assets. This ensures chatbots only retrieve documents the end-user is authorized to view, maintaining compliance and security policies. Data remains within a governed environment, eliminating risks associated with moving it to external vector databases.
Lakebase: Serves as a managed Postgres database for operational workloads, app state, chat history, and memory. It supports low-latency reads and writes, including pgvector for efficient retrieval, keeping app data close to its source.
Agent Bricks: Offers tools for building, deploying, and governing enterprise AI agents that interact with this secure, governed data.
Databricks Apps: Provides a hosting and deployment environment for these secure internal data and AI applications, ensuring robust operations and scalability.

When to Use It

Building internal knowledge chatbots for employee support and information retrieval.
Developing AI assistants that access proprietary customer data for improved service.
Creating agents to automate tasks requiring interaction with governed internal documentation.
Any scenario demanding secure AI integration with sensitive, private enterprise data.

When Not to Use It

For simple, public-facing chatbots that do not require access to private, governed enterprise data.
When the primary need is only basic LLM API integration without custom model fine-tuning or complex data grounding.
For small-scale, personal projects where enterprise-grade governance and scalability are not required.

Recommended Databricks Stack

Unity Catalog: For data, model, tool, app, agent governance, permissions, and lineage.
Lakebase: Operational Postgres for app state, memory, and vector search.
Agent Bricks: For building, deploying, and governing AI agents.
Databricks Apps: For secure internal application hosting and deployment.
MLflow: For evaluation, tracing, and monitoring of agents.
AI Gateway: For model access, routing, and guardrails.

Related Use Cases