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Multiple customer SSO configurations per Workspace

Allow a Featurebase Workspace to configure and manage multiple customer-facing SSO setups instead of relying on one global SSO login URL and JWT configuration. This would support companies that have multiple products, brands, portals, or customer identity systems within one Workspace. Each SSO setup should be able to have: Its own login URL and JWT verification configuration Routing rules that associate it with specific brands, custom domains, Messengers, Help Centers, or public portals Support for returning the customer to the correct surface after authentication Featurebase should resolve the appropriate SSO setup from the active brand or domain while keeping the customer data and product configuration centrally managed in one Workspace. This request concerns customer-facing SSO and is separate from Enterprise SSO used by teammates to access the Featurebase dashboard.

Markus Palm 30 days ago

1

General

In Progress

Unified knowledge management for Help Center and Fibi AI Agent

Knowledge is currently split between Help Center articles and Fibi AI Agent Training Data. They live in separate dashboards, use different content models, and cannot be searched, maintained, or automated as one source of truth. Create one Knowledge dashboard for all reusable support content, similar in concept to Intercom's Knowledge workspace. Common content types: public Help Center article, internal article, snippet, uploaded document, and external source. Choose where each item is used: Help Center, Fibi, and Copilot. An internal or Fibi-only article must never be publicly visible in the Help Center, but should be available to Fibi when answering. Unified management: search, filters, folders, bulk actions, and configurable columns for type, collection, audience, publication status, language, author, last updated date, and where the content is used. Consistent editing and lifecycle: one formatting model, drafts and publishing where relevant, source freshness, and clear ownership. Complete API and MCP coverage: list, search, create, update, delete, publish, and manage metadata for every supported content type. The goal is to stop treating public documentation and AI knowledge as separate systems. Teams should be able to create knowledge once, control who or what can use it, and maintain everything from one place.

Markus Palm 21 days ago

Help Center

Restrict teammates to specific brands in multi-brand workspaces

In a multi-brand Enterprise workspace, allow admins to restrict which teammates can reply to conversations for a given brand. For example, some teammates should only be able to access and reply to Brand A conversations, while others can be granted access to Brand A and Brand B. Today, Assignments controls routing (who a conversation gets assigned to) but does not enforce a hard restriction on who is allowed to reply to a given brand's conversations. Teammates without brand access can still reply. Requested by a prospect running a white label, multi-brand support setup on behalf of their own customers, where different support staff should only be able to touch specific brands' conversations.

Anuj K 11 days ago

Feedback & Roadmaps

Disabling the Help Center should also stop article suggestions and public access

Today, "disabling" the Help Center only removes the module from the portal menu (Settings β†’ Branding β†’ Portal menu β†’ Delete). The content itself stays live, so after disabling it customers still see: Articles suggested during ticket and conversation creation. Articles reachable and indexable on the public Help Center URL. The expectation is that deactivating a module makes its content and all linked functionality inactive, in one action. Requested behaviour A single workspace-level switch that takes the Help Center fully offline: no article suggestions in the Messenger or ticket forms, no public article URLs, no search-engine indexing. Apply the same principle to the other modules, so turning one off never leaves a surface still serving its content. Make the current behaviour explicit in the interface, since the existing control is a navigation change rather than a content switch. Current workarounds are partial: the Messenger Help module and article search card can be turned off individually in Settings β†’ Support β†’ Messenger β†’ Customization, and articles can be restricted to Team members one at a time, which removes them from suggestions and search indexing but cannot be applied in bulk. Raised by a customer evaluating Featurebase who deactivated the Help Center and found the articles still active.

Stefan Pasel about 1 hour ago

Help Center

Git repository integration for AI agent training data

Connect a Git repository (GitHub, GitLab) as a first-class knowledge source for the Fibi AI Agent, so that training content is maintained where the documentation already lives. Requested behaviour Connect one or more repositories and select file patterns (for example docs/**/*.md). Re-index on push, and show the diff between the previous and current version of each training file so reviewers can see exactly what changed. Preserve manual edits made in Featurebase across re-indexing, rather than overwriting them. Report per-file index status so a failed file is visible without opening every entry. A customer evaluating Featurebase raised this after comparing with Gleap's Code Repositories feature, where a connected repo generates knowledge entries automatically on each re-index and manual edits are kept. Today the Training Data API and MCP make this possible to build from the customer side using stable externalId values, but there is no native connector, no change detection and no diff view. Related: Source Markdown Files from Git Repositories (Help Center scoped) and APIv2 for Fibi Training Data Management.

Stefan Pasel about 1 hour ago

Fibi AI Agent

Reliable bulk upload of Fibi training files

Uploading more than two training files at once in Dashboard β†’ Automations β†’ Training Data is unreliable. Files fail very quickly and land in a Failed state, even for small files (for example a 1.8 KB Markdown file that reported 0 words). Failed files then have to be removed one by one and re-uploaded, which makes seeding an agent with a real documentation set slow and error prone. Requested behaviour Multi-file selection should upload reliably, with queueing or throttling instead of parallel failures. Show the actual reason a file failed (size, type, conversion error, rate limit) rather than a generic Failed label. Offer a retry action on failed files instead of requiring manual delete and re-upload. Reported by a customer evaluating Featurebase who is loading a Markdown documentation set into the AI agent.

Stefan Pasel about 1 hour ago

Fibi AI Agent