Embed Governed AI in Your Existing Chat Interface or Portal
Embedding governed AI chat in an existing portal can keep employees inside the tools they already use. But “keep our interface” can mean two different projects: add an AI component to the current application, or retain a custom chat screen and connect it to a different backend.
That distinction affects implementation scope, authentication, conversation history and ongoing support. For buyers comparing enterprise AI chat platforms, resolve it before evaluating a polished demo. Jarvis Chat documents embedding options; the checklist below helps turn that capability into a practical evaluation.
Choose between an embedded component and a custom UI
An embedded component supplies the chat experience inside your portal. A custom-UI integration keeps your existing screen but connects its messages and interactions to another service.
| Approach | What you keep | What needs confirmation |
|---|---|---|
| Embed a provided chat UI | The surrounding portal and its navigation | Styling, sign-in, supported component options and accessibility |
| Connect your custom chat UI | Your conversation screen and interaction design | API compatibility, streaming, files, tool actions and error handling |
Jarvis lists a JavaScript SDK, Web Components and iFrame support. Its frontend embedding demonstration shows the product direction. That does not establish that any existing chat backend can be replaced without adaptation.
Bring the current application’s technology, required chat features and identity setup to the first discussion. Include uploads, citations, conversation export and any actions beyond sending text.
Preserve the user’s identity, not just the portal login
Being signed into a portal does not by itself prove that the AI component knows the user or enforces the same permissions. Trace the identity from the application through the chat session to each model, document source and tool.
Ask how session credentials are issued and refreshed, what logout does, and whether user A can access user B’s conversation. Permanent provider or channel secrets should not be placed in browser code. Microsoft’s web-channel security guidance illustrates token exchange for a browser client; it describes Copilot Studio, not Jarvis’s specific implementation.
If an embedded frame exchanges messages with the host page, review the communication boundary too. MDN’s postMessage guidance explains checking origins and targeting messages precisely. Treat this as an integration check where that mechanism is used, not an assumption about every embed.
Check permissions and model routing together
Test the same question with users who have different document access. The result should reflect their permissions, not the privileges of a shared integration account. Follow a citation back to its source and verify that the requester can open it.
For connected actions, review the Jarvis Registry and Governed AI layer alongside the chat interface. A visible button and the authority to execute its action are separate things.
Hosting an interface in your own cloud is also different from hosting its model. If configured to call an external model API, the request goes to that provider. Confirm allowed models, transmitted context, tool destinations and log retention before entering sensitive data.
Test the interface where employees will use it
A standalone demo cannot show whether the embedded experience fits your actual portal. Test it with the site’s navigation, a narrow screen and realistic conversation length.
Check that users can open the chat, send a message, reach citations and dismiss it using a keyboard. W3C’s keyboard-accessibility guidance provides a useful baseline. Also review focus visibility, readable errors, scrolling and whether the chat obscures important controls.
Ask what happens after token expiry, an interrupted connection or a failed tool call. A useful response should tell the employee whether the request failed, remains in progress or needs to be retried. Measure task completion and repeated failures, not only the number of messages sent.
Define history and handoff before rollout
If keeping old conversations matters, identify who owns them, their storage format and their access rules. Confirm supported import behavior rather than assuming history moves with the interface. Retention, deletion and export should also be part of the handoff.
For a pilot, choose one internal application, one user group and a small set of permitted sources or actions. Agree who maintains the embed, identity integration and backend after launch, and how changes will be tested.
Share your portal, required chat features and access rules in a Jarvis integration discussion. That makes it possible to evaluate the appropriate route without committing to a new interface or promising a drop-in replacement prematurely.
References
- Microsoft: web and Direct Line channel security
- MDN: cross-origin messaging with postMessage
- W3C: keyboard accessibility
Questions about embedded enterprise AI
Can Jarvis Chat be embedded in an existing application?
Jarvis documents JavaScript SDK, Web Component and iFrame options. Confirm which option fits your application, identity setup and user-experience requirements.
Is embedding the same as replacing our current chat backend?
No. Embedding places a provided interface inside your application. Keeping a custom interface while changing its backend is a separate integration that needs API and feature compatibility review.
Does a customer-hosted interface keep every model request private?
No. Requests sent to an external model API follow that provider's data path and terms. Review the selected models, tool connections and logging configuration separately from interface hosting.
Can we keep our existing conversation history?
Treat history migration as a separate requirement. Confirm formats, permissions, retention and supported import behavior; embedding alone does not establish that old conversations can be moved.
