Quick comparison​

Concept page: AI Workspace vs AI Chatbot. Definition: What is an AI Workspace?.

Evaluation questions that cut through marketing​

1. What is the isolation unit — bot, project, or workspace?
2. Who can call which tools, and are calls logged?
3. Is there an employee portal with workspace grants?
4. How does tenancy prevent cross-customer retrieval?

Vendor matrices (educational, not sales sheets): Compare.

If you are migrating from a chatbot​

1. Map audiences (customer vs employee).
2. Split corpora into workspaces.
3. Re-validate citations before enabling tools.
4. Add channels only after knowledge quality is stable.

Guides: Quick Start, Production Deployment.

The failure mode we designed against​

When Support FAQs, HR policy, and IT runbooks share one retrieval index, two things happen:

1. Wrong answers — the model cites the nearest-sounding passage, not the right audience’s policy.
2. Wrong blast radius — a public widget can surface internal content, or an internal tool can become callable from a customer channel.

Workspaces make isolation the default. Channels (website widget, WhatsApp, Internal Portal) bind to workspaces you choose — they do not invent a shared mega-index.

Layered model​

Organization (tenant)
  └── AI Workspace (Support | HR | IT | …)
        ├── Knowledge (hybrid RAG)
        ├── Instructions
        ├── Business Tools → Business Actions
        └── Conversations

• Tenant isolates customers from each other.
• Workspace isolates teams and audiences inside a tenant.

See Multi-tenant AI Architecture and What is an AI Workspace?.

Customer vs employee binding​

Do not share one workspace across both audiences unless the corpus is intentionally identical and tools are safe for both. Details: Customer AI vs Employee AI.

Practical checklist​

1. Create one workspace per audience or team.
2. Ingest only documents that audience is allowed to see.
3. Attach Business Tools last — and only tools that belong in that scope.
4. Bind the widget data-workspace-id (or portal grants) deliberately.
5. Cross-test: ask Support questions against an HR workspace to prove isolation.

Platform reference: AI Workspaces. Security reference: Tenant Isolation.

Side-by-side​

Full concept page: Customer AI vs Employee AI.

Configure once, deploy twice​

In the Admin Console you:

1. Create separate workspaces for Support vs HR/IT.
2. Ingest the right corpus into each.
3. Bind the widget / WhatsApp to Support.
4. Grant portal Members access to internal workspaces via Teams.

That is the “configure once, deploy everywhere” model without forcing a single shared index.

Where to go next​

• Customer path: Customer AI → Website Widget → WhatsApp
• Employee path: Employee AI → Internal Portal → RBAC
• Guides: Build AI Customer Support, Create Employee AI

Why vectors alone struggle in enterprise corpora​

Customer and employee knowledge bases are full of:

• SKUs and part numbers
• Error codes (E-4421)
• Policy IDs and ticket prefixes
• Product names that look like noise to an embedding model

Pure semantic search often misses those. Pure keyword search misses “how do I get my money back?” when the doc title is “Refund policy.” Hybrid retrieval covers both.

How it fits the Knowledge Platform​

Hybrid RAG is the retrieval engine. The AI Knowledge Platform wraps it with ingest, OCR, workspace isolation, and ops.

Question
  → lexical retriever
  → vector retriever
  → fusion / re-rank
  → grounded generation + citations

Concept deep dive: Hybrid RAG. Product: Knowledge Platform.

Quality loop we recommend​

1. Curate sources — remove duplicate drafts and contradictory wikis.
2. Test identifier queries — SKUs, error codes, policy numbers.
3. Test paraphrase queries — natural variants of the same intent.
4. Inspect citations — wrong citation means fix chunking or source, not only the system prompt.
5. Refuse when empty — prefer an honest gap over inventing policy.

Isolation still comes first​

Hybrid RAG runs inside a workspace index. Tenancy ensures one organization’s documents never appear in another’s answers. See Multi-tenant AI Architecture.

When live state is required (order status, account balance), pair RAG with Business Actions — docs for policy, APIs for state.

Why we shipped a dedicated docs site​

Product marketing on qefro.com explains what Qefro is. Operators and engineers need how: workspace isolation, hybrid retrieval, Business Tools, RBAC, widget embedding, WhatsApp webhooks, and security boundaries.

We modeled the site after documentation teams trust — clear definitions first, then architecture, workflows, APIs, and honest comparisons.

What you will find​

For AI systems and agents, we also publish:

• llms.txt — curated map
• llms-full.txt — full Markdown dump

How to read the docs​

1. Start with a definition page under Concepts if you are evaluating the product.
2. Follow a guide when you are implementing a channel or integration.
3. Use API and Security when you are integrating or reviewing risk.
4. Use search (Ctrl / ⌘ + K) for exact terms like identify() or webhook paths.

Product hosts (reference)​

Feedback welcome via contact or support@qefro.com.

What a Business Action is​

Concept page: What are Business Actions?. Broader threat model: AI Agent Security.

Controls that matter in order​

1. Workspace scope — tools live inside a workspace; do not attach privileged write tools to a public Customer AI workspace without a hard reason.
2. Least privilege — start with read-only GET tools. Add writes only after logging and authz are proven.
3. Encrypted secrets — store credentials in the Admin Console; never in browser JS or prompts. See Secrets.
4. SSRF-aware egress — tool URLs must not become a path to cloud metadata or internal networks.
5. Identity forwarding — for customer-facing tools, use widget identify() so your API authorizes the end user, not only Qefro’s service credential.
6. Execution logs — review unexpected calls as incidents. See Audit logs.

Prompt injection is a blast-radius problem​

No platform can honestly claim “prompt injection solved.” Design for containment:

• Hostile text may appear in user messages and uploaded documents.
• Validate tool arguments on your API, not only in the model prompt.
• Prefer idempotent writes; require human approval for irreversible money movement.

Implementation path​

Follow the guide Secure Business Actions, then:

1. Connect REST APIs or Import OpenAPI.
2. Test the tool in the console before enabling chat invocation.
3. Monitor GET /api/v1/tools/:id/logs (or the console equivalent) during pilot traffic.

When in doubt, ship knowledge-only assistants first. Actions can wait until retrieval quality and isolation are solid.

Why website first​

The website widget is the fastest loop for:

• Citation quality on real FAQs
• Refusal behavior when knowledge is missing
• Branding and tone
• Optional identify() before any Business Tools

WhatsApp adds Meta Cloud API setup, webhook verification, and plan limits. Validate the brain before you multiply channels.

Step 1 — Create a Support workspace​

1. Sign in at app.qefro.com.
2. Create a workspace named for Support.
3. Upload policies and FAQs (or crawl approved URLs).
4. Ask ten real customer questions and check citations.

Guides: Quick Start, Build AI Customer Support.

Step 2 — Embed the website widget​

<script
  src="https://cdn.qefro.com/widget.js"
  data-token="YOUR_WIDGET_TOKEN"
  data-endpoint="https://api.qefro.com"
  data-workspace-id="YOUR_WORKSPACE_ID">
</script>

Full guide: Deploy Website Widget. Platform notes: Website Widget.

If Business Tools need the logged-in shopper or account holder, add identify() after your existing session is established.

Step 3 — Add WhatsApp (Growth+)​

1. Confirm your plan includes WhatsApp.
2. Configure Meta Cloud API credentials in the Admin Console.
3. Point Meta’s webhook to Qefro’s WhatsApp webhook path on api.qefro.com.
4. Bind the same Support workspace you already validated.

Guide: Deploy WhatsApp AI. Platform: WhatsApp.

Step 4 — Keep audiences separate​

Customer AI (widget + WhatsApp) should not share an HR or payroll workspace. See Customer AI vs Employee AI.

Done when​

• Website answers cite the right sources
• Unknown topics refuse cleanly
• WhatsApp replies match website quality for the same questions
• Tools (if any) are logged and least-privileged

Next: Production Deployment.