Branded Help Center & AI Widget in 4 Mins
https://faqhub.io
Turn messy docs into an in-app help center and floating widget. Full editorial control + hallucination-proof AI answers. Stop support ticket drag.
Turn messy docs into an in-app help center and floating widget. Full editorial control + hallucination-proof AI answers. Stop support ticket drag.
The secondary verification step is the part that stands out most to me. Using the CMS as the approved source of truth already reduces a lot of risk, but checking the generated answer against the actual source text before showing it feels like the stronger safeguard.
I’m curious how you handle borderline cases where the source technically supports the answer, but only partially or with important context missing. Do those get declined automatically, or does the system return a more cautious answer with the relevant source attached?
Great question! Borderline cases are exactly where standard RAG models get into trouble.
FAQ Hub handles this through a combination of strict confidence thresholds and mandatory source attribution:
Source Attribution by Default: FAQ Hub always attaches the underlying CMS source article(s) to every generated response. The user gets the grounded answer alongside direct links to the full documentation for complete context.
Strict Threshold-Based Declines: If essential context is missing or answer confidence drops below threshold, the system explicitly declines to guess or fill in the blanks.
Contextual Escalation: Instead of fabricating missing details, it prompts the user to connect with a human and passing the full conversation history to Slack, Teams, email, or an n8n workflow (this is so the agent is immediately notified. FAQ Hub natively handles livechat).
By pairing strict thresholds with mandatory source links, users get accurate facts with full context attached, and you never risk serving partial truths as factual claims.
Does this answer you question?
"Hallucination-proof" is a strong claim worth understanding better, is that constrained retrieval only answering from the ingested docs (and declining when it's not covered), or is there an actual verification step checking the generated answer against source text
before it's shown? Those are very different guarantees, and the first one is still improvable to "much less likely to hallucinate" rather than fully proof.
The 4-minute setup claim is the other one I'd want to see hold up in practice, especially for docs that aren't clean markdown, PDFs, scattered Notion pages, or a mix of formats tend to be where "instant" setup tools slow down the most.
Great questions, and fair pushback on the wording. Here is how FAQ Hub handles both in practice:
It isn't just standard constrained retrieval. FAQ Hub uses a two-stage pipeline:
You're completely right that "strict hallucination control with multi-stage verification" is the precise engineering reality over absolute "proof."
The 4-minute claim refers to going from raw assets to a live, embeddable widget:
While cleaning up unstructured or fragmented Notion pages will always require brief human review, the ingestion engine handles the tedious structuring work so you can test a functional widget on your frontend almost immediately.