Grounding
Every answer shows its work
Trust in AI answers comes from provenance. Every BeforeQuery answer shows the exact source paragraphs it was drawn from — and when the knowledge isn't there, the assistant says so instead of inventing something.
Capabilities
Grounding, end to end
From retrieval to the moment a reader clicks a source
01
Provenance built in
Source-Backed Answers
- Numbered sources assembled into the answer context
- Structured sources array on every response: URL, title, excerpt
- Click through to the exact source page
- Same sources across widget, bots, portal, API, and MCP
- Related articles surfaced alongside answers
02
No confident nonsense
Honest Uncertainty
- Confidence scoring on every answer
- Low-confidence fallback instead of hallucination
- Configurable fallback routes users to support
- Unanswerable questions logged as knowledge gaps
- Form deflection only deflects well-grounded answers
03
Audit any answer
Verifiability
- Conversation history with the sources that supported each answer preserved
- Traces show retrieved chunks behind each answer
- User feedback (helpful / not helpful) per message
- LLM-judge evals score answer quality over time
- Audit-friendly for compliance review
Why grounding beats guessing
The difference between an answer and a liability
A support answer that invents a refund policy, a fee schedule, or an API parameter costs more than no answer at all. BeforeQuery generates answers only from content you indexed and approved, shows the source, and falls back transparently outside that boundary — which is what makes it deployable in fintech, healthcare, and the public sector.
- Answers restricted to your indexed, approved content
- Users verify claims with one click on the source
- Compliance can reconstruct any conversation from traces
- Deflection metrics count only confident, grounded answers
- Gaps become a prioritized backlog, not silent failures
What teams say
Deployed in production.
“Deployed on our docs site in an afternoon. Every answer shows the source, and the abstention gate means we've never had a customer complain about a made-up answer.”
Priya Nair
Head of Customer Support · Supabase
“The knowledge base connected to our Slack, Confluence, and helpdesk in one setup. On-call teams get the same grounded answer whether they ask in chat, in the widget, or from Cursor.”
Tom Richter
IT Operations Manager · Grafana Labs
“The gap analytics turned into a real docs backlog. Deflection went up because we finally knew which pages were missing — the AI told us.”
Ana Castillo
VP of Customer Experience · Clerk
Frequently Asked Questions
Common questions about Traceable Answers
Each source includes the URL, document title, and the excerpt used, delivered as a structured array alongside the answer — so any UI, bot, or API consumer can render clickable, verifiable sources.
Answers are generated from retrieved chunks of your indexed content, the prompt requires grounding in those sources, and confidence scoring gates the result. Below the threshold, users get a transparent fallback message you configure — not a guess.
Yes. Conversations are stored with the sources that supported each reply, traces record the retrieved passages behind each answer, and workspace audit logs cover administrative actions — enough to reconstruct any answer for review.
Yes — widget, Slack/Discord/Teams bots, the Ask portal, the public API, and MCP responses all carry the same structured source array from the same pipeline.
They become data. Gap analytics collect unanswered and low-confidence questions into a prioritized list, and doc proposals can draft the missing pages for your team to review and publish.
Ship AI answers you can defend
Grounded, source-backed, and auditable — try it on your own knowledge for free.
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