What it does
Automatically creates customized pricing tables for proposals using AI based on project scope, pulling from your product catalog and applying discounts, bundling logic, and formatting to match your proposal template.
Why I recommend it
Building pricing tables manually is tedious and error-prone, especially with volume discounts, add-ons, and custom bundles. AI generates accurate tables instantly from scope descriptions, reducing proposal turnaround time from hours to minutes.
Expected benefits
- 60-80% faster proposal creation
- Zero pricing errors or forgotten line items
- Consistent pricing presentation
- More time for strategy vs spreadsheet work
How it works
Sales rep provides project scope description -> AI analyzes requirements and maps to product catalog -> calculates pricing with applicable discounts and bundles -> generates formatted pricing table -> inserts into proposal template for review.
Quick start
Create a spreadsheet of your products with pricing and common bundle rules. For your next 3 proposals, describe the scope to ChatGPT and ask it to generate a pricing table. Refine the prompt, then automate.
Level-up version
Pull live pricing from product database or Stripe. Automatically apply discount tiers based on deal size. Include optional add-ons and upsells. Generate multiple pricing scenarios (basic, standard, premium). Add ROI calculations and comparison tables.
Tools you can use
AI: ChatGPT API, Claude API
Pricing data: Airtable, Google Sheets, database
Proposals: PandaDoc, Proposify, DocuSign
CRM: GoHighLevel, HubSpot, Salesforce for deal context
Automation: Zapier, Make, n8n
Also works with
Proposal tools: Qwilr, Better Proposals, GetAccept
CPQ: Salesforce CPQ, DealHub for complex pricing
Spreadsheets: Excel, Google Sheets for table generation
Technical implementation solution
- No-code: Sales rep fills form with scope -> Zapier sends to ChatGPT with product catalog context -> parse pricing table response -> insert into PandaDoc template.
- API-based: CRM webhook on deal stage change -> extract requirements -> query product catalog API -> Claude API generates pricing logic -> format as table -> insert into proposal document via API.
Where it gets tricky
Keeping product catalog and pricing current, handling custom/negotiated pricing accurately, ensuring discount logic is applied correctly, and formatting tables to match proposal aesthetics across different tools.
