Guide
Custom integrations: connecting enterprise systems to AI
AI is only as useful as the data and systems it can reach. A model that can't see your documents, databases, or internal apps is limited to generic answers, while a model wired into your stack can reason over your actual business context. Custom integrations are what turn a capable model into a tool that answers questions about your organization.
Why integrations matter
Most of the value an enterprise wants from AI lives behind a login: in file stores, ticketing systems, CRMs, wikis, and operational databases. Without integrations, users end up copying and pasting context into a chat window, which is slow, error-prone, and leaves sensitive data scattered across prompts.
Integrations close that gap. By giving the model controlled access to the systems where work already happens, answers become grounded in real data instead of guesses, and the AI can act as a layer over your stack rather than a separate silo.
Common integration patterns
Enterprise AI integrations tend to fall into a few recurring patterns:
- Cloud storage: connecting document stores so the AI can read and reason over files in their current location.
- Databases: querying structured data so answers reflect live records rather than stale exports.
- Internal apps and APIs: calling your own services so the AI can pull workflow-specific context or trigger actions.
- Knowledge bases: indexing wikis, help centers, and documentation so the model can cite authoritative internal sources.
Keeping integrations governed
Connecting AI to enterprise systems raises the stakes on governance. An integration that ignores permissions can surface data a user shouldn't see, so access controls have to carry through from the source system into the AI's responses.
Good practice is to respect existing roles and permissions, log what the AI accesses, and keep data flows auditable. Integrations should expand what the AI can do without expanding who can see what.
How ChatLite integrates
ChatLite ships with connectors like Google Drive and OneDrive, plus built-in web search and Wolfram Alpha, so the model can reach common data sources out of the box. For everything else, it supports custom integrations and live data feeds to internal systems, so answers are grounded in your data rather than generic training knowledge.
Because ChatLite can be self-hosted, those integrations run inside your own environment, and access stays governed by your existing controls. Explore the available integrations or learn how built-in web search keeps answers current.