Stop losing knowledge when people leave
Institutional memory that survives offboarding.
When a senior teammate leaves, their knowledge leaves too — the quirks, the runbooks-in-their-head, the 'oh you have to ask Priya about that.' The org relearns the same lessons expensively, usually during an incident.
BeforeQuery makes written knowledge actually retrievable, which changes the incentive to write things down: a doc that answers questions automatically is worth writing. Doc proposals convert recurring unanswered questions into drafted pages, systematically moving tribal knowledge into the indexed, permanent record.
How it works
- 1Index everything already written — repos, wikis, docs, drives — so it becomes retrievable.
- 2Recurring unanswered questions surface in gap analytics.
- 3Doc proposals draft pages that capture the missing knowledge; experts review and accept.
- 4The knowledge survives in the indexed, permanent, citable record.
What you get
Features that make it work
Frequently asked questions
How does this capture knowledge that was never written down?
Gap analytics reveal the questions people keep asking that no doc answers — a map of the tribal knowledge. Doc proposals draft pages for those gaps, and the expert's job shrinks to reviewing a draft instead of authoring from scratch.
Why would people write docs now when they did not before?
Because the incentive changes: an indexed doc answers questions automatically and gets cited, visibly reducing interruptions to its author. Writing becomes leverage instead of a chore nobody reads.
What sources capture the most institutional knowledge?
GitHub repos (READMEs, ADRs, runbooks), Notion and Confluence wikis, Google Drive and SharePoint documents, and even Slack — all supported source types that index into one assistant.
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.
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Source groups keep Product A answers out of Product B chats.
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