Quote Request Assistant
Capture quote requests from forms and emails, extract requirements, and draft initial quote responses or scope documents.
What you need to build this
Connect these tools, give the AI this data, expect these outputs.
Connect
APIs & tools to set up
OAuth, API keys, or webhooks depending on the tool.
Gather
Data sources to pull from
Give to AI
Inputs per run
- Raw quote request (form fields or email body)
- Service catalog with pricing guidelines
- Past quote examples for similar requests
- Qualification criteria (minimum project size, service area, etc.)
Expect
AI outputs
- Structured requirements summary: scope, timeline, budget signals, location
- Qualification score: fit vs. not-fit with reasons
- Draft acknowledgment email with clarifying questions
- New row in quote tracker spreadsheet/CRM
Business problem
Quote requests arrive via website forms, email, and phone messages in inconsistent formats. Staff spend 20–30 minutes per request just organizing requirements before they can price anything.
Expected outcome
Each quote request is automatically logged, requirements extracted into a structured format, and a draft scope/acknowledgment email sent within 1 hour of receipt.
Who it's for: Businesses receiving 5–30 quote requests per week where each quote requires understanding scope before pricing — contractors, agencies, custom manufacturers.
Required data sources
- Website Form SubmissionsView access method →
- EmailsView access method →
- SpreadsheetsView access method →
- CRM RecordsView access method →
What data goes into the AI
- Raw quote request (form fields or email body)
- Service catalog with pricing guidelines
- Past quote examples for similar requests
- Qualification criteria (minimum project size, service area, etc.)
What the AI should output
- Structured requirements summary: scope, timeline, budget signals, location
- Qualification score: fit vs. not-fit with reasons
- Draft acknowledgment email with clarifying questions
- New row in quote tracker spreadsheet/CRM
APIs needed to automate this
- Gmail APIAuth & setup →
- Google Sheets APIAuth & setup →
- HubSpot APIAuth & setup →
- OpenAI APIAuth & setup →
System behavior
- Triggered on each new form submission or labeled email
- Extracts and structures requirements — does not generate final pricing
- Flags unqualified requests with reason (out of area, too small, etc.)
- Creates tracker row and acknowledgment draft within 1 hour
- Escalates complex or high-value requests to owner immediately
No-code vs low-code vs custom
No-code
Lowest effort
Non-technical owners, quick validation, under 50 runs/day
3 options listed
Low-code
Medium effort
Operators comfortable with Zapier/n8n, need more control
2 options listed
Custom build
Highest effort
High volume, custom logic, or strict data privacy requirements
4 options listed
| No-code options | Low-code options | Custom build stack |
|---|---|---|
| Zapier: form submit → ChatGPT extract → Sheets row + Gmail draft | n8n: form webhook → OpenAI structured extraction → CRM + email | Webhook for form submissions + Gmail label trigger for email quotes |
| Typeform + OpenAI integration | Make.com with form parser and GPT modules | OpenAI structured JSON output for requirement extraction |
| HubSpot form + AI enrichment | — | Sheets/CRM API for tracker update |
| — | — | Gmail API for acknowledgment draft |
Start with no-code to validate the workflow. Move to low-code when you hit rate limits or need branching logic. Custom build when volume, privacy, or integration depth requires it.
Step-by-step implementation
- 1Define quote tracker columns and qualification criteria
- 2Connect form and email intake channels
- 3Build extraction prompt with 5–10 past quote examples
- 4Set up auto-acknowledgment draft with clarifying questions
- 5Run in review mode: AI extracts and logs, human reviews before sending
- 6Track quote-to-close rate and refine qualification criteria quarterly
Risks and limitations
- AI may miss critical requirements leading to under-quoted projects
- Auto-acknowledgment with wrong clarifying questions looks unprofessional
- Qualification AI may reject good leads with non-standard requests
- Pricing guidelines in prompts could leak through draft emails
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