Knowledge Base + RAG for Conversational AI Your bot, that knows your docs.

Most chatbot ‘knowledge bases’ are static FAQs that drift out of date. EnableX Knowledge Base connects directly to your live docs (Confluence, Notion, SharePoint, S3, public web pages) and uses retrieval-augmented generation (RAG) to answer customer questions from the source of truth. When your docs update, the bot’s answers update automatically. With hallucination guards that prevent the bot from making up answers when the docs don’t have one.

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Live KB sync · 6 connector types · Hallucination guards · 4 wk to deploy

What sets it apart

Live KB sync

Connectors for Confluence, Notion, SharePoint, S3, Google Drive, public web. KB updates in 5-15 minutes, no retraining, no redeploy.

Citation-grade answers

Bot retrieves the source doc(s), generates an answer, and cites the source. Customer can ask “where did you get that?” and the bot links to the document.

Hallucination guards

Confidence thresholds. If the retrieval score is below threshold, bot says “I don’t have that answer” and escalates to a human. No making things up.

How it works

Connectors

6 connector types out of the box.

Confluence, Notion, SharePoint, S3 / GCS / Azure Blob, Google Drive, public web crawl. Custom connectors via REST API. Sync interval: 5-15 minutes typical, real-time on webhook-supported sources.

  • Confluence + Notion
  • SharePoint + OneDrive
  • S3 / GCS / Azure Blob
  • Google Drive
  • Public web crawl
  • REST API for custom

Six connector types, syncing on a 5-15 minute cadence.

Retrieval

RAG-powered, not static FAQs.

Vector embeddings of every doc chunk. Customer query goes to semantic retrieval (top-k matches), then LLM generates answer grounded in retrieved chunks. Citation IDs included so the customer can ask for source.

  • Vector embeddings (your choice of model)
  • Semantic + keyword hybrid retrieval
  • Top-k chunk retrieval
  • LLM grounding with citation
  • Per-document permission filters

Every answer grounded in a retrieved, cited chunk.

Guards

No making things up.

Confidence threshold per query. If retrieval score is below threshold, bot says “I don’t have that answer” + offers human handoff. Optional refusal list (regulatory disclosures, legal phrasing). PII redaction in retrieved chunks.

  • Retrieval-confidence threshold
  • “I don’t know” + handoff
  • Refusal list (regulatory)
  • PII redaction in retrieval
  • Per-channel guard tuning

Below threshold, the bot hands off instead of guessing.

Permissions

Doc-level access control.

Bot inherits doc-level permissions from source (Confluence space ACLs, SharePoint groups). Customer A asking the same question as Customer B may get different answers if their entitlements differ.

  • Inherits source permissions
  • Per-customer entitlement filters
  • Audit log of every retrieval
  • Encryption in transit + at rest
  • SOC 2 / ISO 27001 / DPDPA-aligned

Entitlements from your IdP or CRM, carried into every answer.

Use cases

BFSI

Loan + product Q&A

Bot answers loan eligibility, interest rate, fee questions from RBI-aligned docs.

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Insurance

Policy Q&A

Bot answers what’s covered, exclusions, claim process, from policy docs.

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All verticals

Tier-1 customer support

Self-serve from product docs, FAQs, troubleshooting guides.

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Healthcare

Pre-procedure FAQs

Pre-op questions, fasting requirements, insurance coverage, from clinic docs.

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EdTech

Programme + admissions

Programme details, fees, eligibility, from website + brochure.

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SaaS

Product help

In-app + web bot answers from product docs, KB articles, release notes.

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5-15 min KB sync interval
6+ connector types
Citations in every answer
Guards against hallucination

Explore related

Studio Overview

The full Studio platform.

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Visual Flow Builder

Where KB lookup nodes get used.

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50+ Integrations

CRM + helpdesk connectors that pair with the KB.

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Build for me

Professional Services

We connect, tune, and deploy your KB in 4-8 weeks.

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FAQ: Knowledge Base + RAG

RAG (Retrieval-Augmented Generation) is the pattern of retrieving relevant documents from your knowledge base and grounding the LLM’s answer in those documents. Instead of relying on the LLM’s training data (which may be wrong or out of date), RAG fetches the source-of-truth from your docs and asks the LLM to answer using that. EnableX Knowledge Base is RAG-powered.

Confluence, Notion, SharePoint, OneDrive, S3, Google Cloud Storage, Azure Blob, Google Drive, public web crawl. Custom connectors via REST API or webhook for proprietary KBs (your CMS, legacy doc repos, etc.).

5-15 minutes typical for periodic-sync sources (Confluence, Notion, S3). Real-time for webhook-supported sources (any source that supports change notifications). No retraining or redeploy needed when docs change.

Three layers: (1) retrieval confidence threshold, where if the top-retrieved chunks don’t score high enough, bot says “I don’t have that answer”; (2) LLM grounding, where instructions force the LLM to answer only from retrieved chunks, not from training memory; (3) refusal list, where regulatory phrasings (legal disclosures, financial advice) can be force-handed-off to human regardless of retrieval score.

Yes. EnableX inherits doc-level permissions from the source. If a Confluence space is restricted to Sales, only customers with Sales entitlement get answers from that space. Per-customer entitlement filters via your IdP / CRM.

Cloud: 1-2 weeks for connecting 1-2 sources and tuning retrieval thresholds. 3-4 weeks for multi-source KB with permissioning. Most of the work is in tuning retrieval quality on your specific corpus, not in connection.

See EnableX in action.

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