Detect and fix stale documentation
AI-drafted doc updates from real user questions.
The product ships weekly but the docs do not, and nobody owns noticing the drift. Users follow outdated instructions, fail, and lose trust in the entire docs site — which drives even more tickets.
BeforeQuery's doc proposals scan knowledge gaps and drafts new or updated documentation to close them; your team reviews and accepts or dismisses each proposal. Combined with scheduled source syncs that keep the index current, docs maintenance becomes a review queue instead of an archaeology project.
How it works
- 1Scheduled syncs keep the index matched to your current docs.
- 2Gap analytics reveal where users' questions and your docs have drifted apart.
- 3Doc proposal scans draft updates and new pages to close those gaps.
- 4Your team reviews each proposal and accepts or dismisses it.
What you get
Features that make it work
Frequently asked questions
How does BeforeQuery know a doc is stale?
Staleness shows up as unanswerable or poorly answered questions: gap analytics flag topics where user questions outpace your content. Doc proposal scans then draft content to close the specific gaps found.
Does the AI publish doc changes on its own?
No. Proposals sit in a review queue where your team accepts or dismisses each one. Nothing changes in your docs without human approval.
How current is the AI's view of our docs?
As current as your last sync — sources re-index on a schedule, and GitHub/GitLab sources re-index via webhook on push, so the assistant answers from the docs you actually ship.
How long does setup take?
Most teams are live the same day. You connect a source — a docs URL, GitHub repo, Notion workspace, or a file upload — and BeforeQuery crawls, normalizes, chunks, and embeds the content automatically. The website widget is a single script tag, and the Slack/Discord bots install in a few clicks. There is no model to fine-tune and no infrastructure to run.
Are answers backed by citations?
Yes. Every answer includes a structured citations list with the URL, title, and excerpt of each source used, and the answer text cites sources inline. Users can click through to verify any claim against the original document — which is what makes the answers trustworthy enough for support, sales, and internal use.
Is my content used to train AI models?
No. Your indexed content, embeddings, and conversations are isolated to your workspace and are only used to answer your own questions. Content is never shared across customers, and data is encrypted at rest and in transit. Enterprise plans add OIDC/SAML SSO, audit logs, and data-residency options.
Which knowledge sources can I connect?
18+ source types: websites, GitHub, GitLab, Notion, Confluence, Google Drive, SharePoint, Shopify, Zendesk, Jira, Linear, Salesforce, Slack, Discourse, Stack Overflow, YouTube, OpenAPI specs, and direct file uploads (PDF, Markdown, TXT, HTML). Sources sync on a schedule, and GitHub/GitLab can re-index automatically via webhooks on every push.
What happens when the AI is not confident in an answer?
Answers are confidence-scored. When retrieval confidence falls below your threshold, BeforeQuery returns a transparent, configurable fallback — typically pointing users to your support channel — instead of guessing. Those unanswered questions are logged in knowledge gap analytics so you can close the gap in your docs.
Related use cases
Make docs nobody reads actually useful
Turn a passive docs site into an interactive answer engine.
Discover what your docs are missing
Every unanswered question becomes a prioritized docs backlog.
Measure documentation ROI
Show which docs answer questions and deflect tickets.
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