How do I set up an on-site assistant to answer product questions and route qualified leads?

To set up an on-site assistant that answers product questions and routes qualified leads, think of it as two jobs in one:

1. Product concierge — answers FAQs, recommends products, handles comparisons.
2. Lead qualifier / router — collects contact info and hands off high-intent visitors to sales or CRM.

Here’s a practical setup:

1) Define the assistant’s scope

Decide exactly what it should do:

• Answer product FAQs
• Recommend products based on visitor needs
• Check availability, pricing, specs, compatibility
• Capture lead details
• Route sales-ready leads to the right team
• Escalate to human support when needed

Also define what it should not do:

• Promise custom pricing without approval
• Make unsupported claims
• Handle legal/medical/financial advice
• Process sensitive payment data unless your stack is built for it

2) Gather the knowledge base

The assistant is only as good as its content. Feed it:

• Product catalog
• Specs, manuals, comparison charts
• Pricing rules
• Shipping/returns policies
• Warranty info
• FAQ pages
• Sales playbooks and qualification criteria
• Internal docs for routing rules

Best practice: use a retrieval-based setup so the assistant answers from approved documents instead of making things up.

3) Choose lead qualification criteria

Decide what makes a lead “qualified.” Common signals:

• Budget range
• Company size
• Use case
• Timeline
• Location
• Need severity / intent
• Product fit

Example:

• Qualified lead = budget over $X, timeline under 3 months, and specific product interest
• Warm lead = interested but not ready
• Unqualified = out of geography, outside target segment, or no real need

4) Design the conversation flow

Build the assistant to:

1. Greet the visitor
2. Ask what they’re looking for
3. Answer product questions
4. Offer recommendations
5. Ask 2–5 qualifying questions when appropriate
6. Capture contact info
7. Route to the right person/system

Example flow:

• “What are you looking for?”
• “I can help compare options. Is this for personal use or a business?”
• “What’s your timeline?”
• “Would you like a quote or a callback?”

5) Route leads automatically

Set rules for where leads go:

• High-intent enterprise lead → Salesforce / HubSpot → assigned to enterprise rep
• Local lead → territory-based rep
• Technical question → product specialist
• Low-intent lead → nurture sequence/email campaign

You can route based on:

• Product category
• Geography
• Company size
• Priority score
• Availability of sales reps

6) Connect it to your systems

Typical integrations:

• CRM: Salesforce, HubSpot, Zoho
• Calendar: book meetings directly
• Email/SMS: send follow-ups
• Chat platform: website widget, app chat, kiosk, tablet
• Analytics: track questions, conversion, drop-off points

7) Add guardrails

To keep it accurate and safe:

• Use approved content only
• Add confidence thresholds
• Escalate uncertain questions to humans
• Log conversations for review
• Require human approval for offers/discounts
• Include compliance disclaimers where needed

8) Train it on real customer language

Use:

• Past chat logs
• Support tickets
• Sales call transcripts
• Search queries
• Common objections

This helps it recognize how customers actually ask things, not just how your docs phrase them.

9) Measure performance

Track:

• Answer accuracy
• Lead capture rate
• Qualified lead rate
• Conversion to meeting/demo
• Escalation rate
• CSAT
• Time to first response

Use these metrics to improve prompts, content, and routing rules.

10) Start with a pilot

Launch on:

• One product line
• One geography
• One customer segment

Then expand once you’ve validated:

• The answers are correct
• Leads are being routed properly
• Sales likes the quality of the handoffs

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Simple recommended architecture

A good modern setup looks like this:

• Website widget / in-store tablet
• LLM assistant
• Knowledge retrieval layer
• Lead qualification logic
• CRM integration
• Human handoff / live chat escalation

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Example implementation pattern

If you want a straightforward version:

• Assistant answers from your product docs
• If visitor asks buying questions, it asks 3 qualifying questions
• If lead meets threshold, it creates a CRM record and notifies sales
• If not, it sends them a helpful follow-up email and keeps them in nurture

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Good prompting rule

Give the assistant a strict operating instruction like:

• “Only answer using approved sources.”
• “If unsure, say you’re not sure and offer human handoff.”
• “When a visitor shows buying intent, collect name, email, company, and timeline before routing.”

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If you want, I can also give you:

1. a sample conversation flow,
2. a lead qualification scoring model, or
3. a technical architecture diagram / stack recommendation.