Automated Quote Generation with AI: Build a Quote Engine with Codex GPT-5.3 [2026]

The average B2B quote takes 2-5 days to generate.

That's 2-5 days where your prospect is talking to competitors. 2-5 days where urgency dies. 2-5 days of back-and-forth between sales, finance, and legal.

Meanwhile, the company with automated quoting sends a professional, accurate quote in 15 minutes—while your team is still "checking pricing with leadership."

Let me show you how to build an AI-powered quote engine that turns complex pricing into instant proposals.

Why Manual Quoting Kills Deals

Let's trace a typical quote request:

1. Day 1: Prospect asks for pricing
2. Day 1: Rep checks standard pricing, realizes it needs customization
3. Day 2: Rep emails sales manager for approval on discount
4. Day 2-3: Manager is in meetings, responds next morning
5. Day 3: Rep creates quote in CPQ tool (or worse, Excel)
6. Day 3-4: Quote sent to legal for contract review
7. Day 4-5: Legal returns redlined version
8. Day 5: Quote finally sent to prospect

By day 5, your prospect has already received two competitor quotes.

The fix: AI that knows your pricing logic, understands approval thresholds, and generates compliant quotes instantly.

The AI Quote Generation Framework

An intelligent quoting system does four things:

1. Understands complex pricing (tiers, add-ons, volume discounts)
2. Applies business rules (discount limits, approval requirements)
3. Generates professional documents (branded, legally compliant)
4. Routes for approval only when necessary

Let's build each component.

Step 1: Pricing Intelligence with Claude Code

Your pricing isn't simple. It has:

• Base tiers
• Per-seat pricing
• Volume discounts
• Multi-year commitments
• Add-on modules
• Partner discounts
• Promotional offers

Claude Code can model all of this:

# Pricing engine with Claude Code
class AIQuoteEngine:
    def __init__(self):
        self.base_pricing = load_pricing_config()
        self.discount_rules = load_discount_rules()
        self.approval_matrix = load_approval_matrix()
    
    def calculate_quote(self, requirements):
        """
        Generates optimal quote based on prospect requirements
        """
        
        # Base calculation
        quote = {
            'base_products': [],
            'add_ons': [],
            'discounts': [],
            'total_arr': 0,
            'total_monthly': 0
        }
        
        # Calculate base tier
        tier = self.determine_tier(requirements['seats'])
        base_price = self.base_pricing[tier]['per_seat'] * requirements['seats']
        
        quote['base_products'].append({
            'name': f'{tier.title()} Plan',
            'quantity': requirements['seats'],
            'unit_price': self.base_pricing[tier]['per_seat'],
            'subtotal': base_price
        })
        
        # Add-ons
        for addon in requirements.get('add_ons', []):
            addon_price = self.calculate_addon_price(addon, requirements['seats'])
            quote['add_ons'].append(addon_price)
        
        # Apply discounts
        discounts = self.calculate_discounts(requirements, quote)
        quote['discounts'] = discounts
        
        # Calculate totals
        subtotal = (
            sum(p['subtotal'] for p in quote['base_products']) +
            sum(a['subtotal'] for a in quote['add_ons'])
        )
        discount_amount = sum(d['amount'] for d in discounts)
        
        quote['subtotal'] = subtotal
        quote['discount_total'] = discount_amount
        quote['total_arr'] = subtotal - discount_amount
        quote['total_monthly'] = quote['total_arr'] / 12
        
        # Check approval requirements
        quote['approval_required'] = self.check_approval_requirements(quote, requirements)
        
        return quote
    
    def calculate_discounts(self, requirements, quote):
        """
        Applies all eligible discounts based on business rules
        """
        discounts = []
        subtotal = quote['subtotal']
        
        # Volume discount
        if requirements['seats'] >= 50:
            volume_discount = subtotal * 0.15  # 15% for 50+ seats
            discounts.append({
                'type': 'volume',
                'description': 'Volume discount (50+ seats)',
                'percentage': 15,
                'amount': volume_discount
            })
        elif requirements['seats'] >= 25:
            volume_discount = subtotal * 0.10  # 10% for 25-49
            discounts.append({
                'type': 'volume',
                'description': 'Volume discount (25+ seats)',
                'percentage': 10,
                'amount': volume_discount
            })
        
        # Multi-year commitment
        if requirements.get('contract_years', 1) >= 3:
            commitment_discount = subtotal * 0.20  # 20% for 3-year
            discounts.append({
                'type': 'commitment',
                'description': '3-year commitment discount',
                'percentage': 20,
                'amount': commitment_discount
            })
        elif requirements.get('contract_years', 1) >= 2:
            commitment_discount = subtotal * 0.10  # 10% for 2-year
            discounts.append({
                'type': 'commitment',
                'description': '2-year commitment discount',
                'percentage': 10,
                'amount': commitment_discount
            })
        
        # Partner discount
        if requirements.get('partner_referral'):
            partner_discount = subtotal * 0.05  # 5% partner referral
            discounts.append({
                'type': 'partner',
                'description': 'Partner referral discount',
                'percentage': 5,
                'amount': partner_discount
            })
        
        return discounts

Step 2: Business Rule Enforcement with Codex

Codex GPT-5.3 excels at generating the logic that enforces your business rules:

// Approval matrix generated with Codex
const APPROVAL_MATRIX = {
  // Discount thresholds
  discount: {
    standard: {
      maxPercentage: 20,
      approver: null  // No approval needed
    },
    elevated: {
      maxPercentage: 30,
      approver: 'sales_manager'
    },
    exceptional: {
      maxPercentage: 40,
      approver: 'vp_sales'
    },
    executive: {
      maxPercentage: 50,
      approver: 'cro'
    }
  },
  
  // Deal size thresholds
  dealSize: {
    standard: {
      maxArr: 50000,
      approver: null
    },
    significant: {
      maxArr: 150000,
      approver: 'sales_manager'
    },
    strategic: {
      maxArr: 500000,
      approver: 'vp_sales'
    },
    enterprise: {
      maxArr: Infinity,
      approver: 'cro'
    }
  },
  
  // Special terms
  specialTerms: {
    extendedPayment: {
      trigger: 'net_60_or_greater',
      approver: 'finance'
    },
    customSla: {
      trigger: 'non_standard_sla',
      approver: 'legal'
    },
    dataRequirements: {
      trigger: 'custom_data_handling',
      approver: 'security'
    }
  }
};

async function determineApprovals(quote, requirements) {
  const approvals = [];
  
  // Check discount level
  const totalDiscount = quote.discount_total / quote.subtotal * 100;
  for (const [level, rule] of Object.entries(APPROVAL_MATRIX.discount)) {
    if (totalDiscount <= rule.maxPercentage) {
      if (rule.approver) {
        approvals.push({
          type: 'discount',
          level: level,
          approver: rule.approver,
          reason: `Discount of ${totalDiscount.toFixed(1)}% exceeds standard threshold`
        });
      }
      break;
    }
  }
  
  // Check deal size
  for (const [level, rule] of Object.entries(APPROVAL_MATRIX.dealSize)) {
    if (quote.total_arr <= rule.maxArr) {
      if (rule.approver && !approvals.find(a => a.approver === rule.approver)) {
        approvals.push({
          type: 'deal_size',
          level: level,
          approver: rule.approver,
          reason: `Deal size of $${quote.total_arr.toLocaleString()} requires approval`
        });
      }
      break;
    }
  }
  
  // Check special terms
  if (requirements.paymentTerms >= 60) {
    approvals.push({
      type: 'special_terms',
      approver: 'finance',
      reason: `Extended payment terms: Net ${requirements.paymentTerms}`
    });
  }
  
  return approvals;
}

Step 3: Document Generation

Now let's generate the actual quote document:

# Quote document generator using Claude
async def generate_quote_document(quote, prospect, requirements):
    """
    Generates professional quote document with Claude
    """
    
    prompt = f"""
    Generate a professional sales quote document.
    
    QUOTE DATA:
    {json.dumps(quote, indent=2)}
    
    PROSPECT:
    - Company: {prospect['company_name']}
    - Contact: {prospect['contact_name']}
    - Title: {prospect['contact_title']}
    - Email: {prospect['email']}
    
    REQUIREMENTS:
    - Use case: {requirements.get('use_case', 'SDR automation')}
    - Timeline: {requirements.get('timeline', 'Q1 start')}
    - Special notes: {requirements.get('notes', 'None')}
    
    GENERATE:
    1. Professional cover letter (2-3 paragraphs)
       - Reference specific pain points discussed
       - Highlight ROI based on their team size
       - Create urgency around timeline
    
    2. Quote summary
       - Clear line items with pricing
       - Discounts broken out separately
       - Total clearly displayed
    
    3. What's included section
       - Feature bullet points
       - Implementation support
       - Training/onboarding
    
    4. Terms and conditions summary
       - Payment terms
       - Contract length
       - Valid until date (14 days from today)
    
    5. Next steps
       - How to proceed
       - Contact information
       - Scheduling link for questions
    
    Format as markdown that can be converted to PDF.
    Tone: Professional but warm. We're partners, not vendors.
    """
    
    document = await claude.generate(prompt)
    
    # Convert to PDF
    pdf_bytes = markdown_to_pdf(document, template='quote_template')
    
    return {
        'markdown': document,
        'pdf': pdf_bytes,
        'filename': f'Quote_{prospect["company_name"]}_{datetime.now().strftime("%Y%m%d")}.pdf'
    }

Step 4: Automated Workflow with OpenClaw

Tie it all together with OpenClaw for end-to-end automation:

# Quote automation workflow
trigger: quote_requested

steps:
  - name: gather_requirements
    action: parse_quote_request
    extract:
      - seats
      - add_ons
      - contract_length
      - special_requirements
      
  - name: enrich_prospect
    action: fetch_prospect_data
    sources:
      - crm
      - enrichment_api
      
  - name: calculate_quote
    action: run_quote_engine
    engine: ai_quote_calculator
    
  - name: check_approvals
    action: evaluate_approval_matrix
    on_approval_needed:
      - route_to_approvers
      - notify_rep_of_pending
      
  - name: generate_document
    action: create_quote_pdf
    template: professional_quote
    
  - name: deliver_quote
    condition: no_approval_needed OR approval_granted
    action: send_to_rep
    channel: slack_dm
    attachments:
      - quote_pdf
      - quote_summary
    message: |
      ✅ Quote ready for {prospect_name}
      
      💰 Total ARR: ${total_arr}
      📊 Discount: {discount_percentage}%
      📅 Valid until: {expiry_date}
      
      [Download Quote]({pdf_link})
      [View in CRM]({crm_link})
      
  - name: log_activity
    action: update_crm
    record_type: quote
    attach_document: true

Real-World Example: Quote Generation in Action

Scenario: Rep gets pricing request on a call

Before AI (5-day process):

1. Rep notes requirements
2. Emails pricing team
3. Waits for response
4. Gets Excel with numbers
5. Creates quote in Word
6. Sends to legal
7. Finally delivers quote

With AI (15-minute process):

Rep: "Zenith, I need a quote for Acme Corp. 30 seats, Growth plan, 
      2-year commitment. They want the Analytics add-on."

Zenith: "Generating quote now...

📊 Quote Summary for Acme Corp:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Growth Plan (30 seats)     $54,000/year
Analytics Add-on           $6,000/year
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Subtotal                   $60,000/year

Discounts Applied:
• Volume (30 seats)        -$6,000 (10%)
• 2-year commitment        -$5,400 (10%)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Total ARR                  $48,600/year
Monthly                    $4,050/month

✅ No approval required (within standard discount threshold)

[Download PDF] [Send to Prospect] [Edit Quote]"

The rep can review, customize if needed, and send—all within minutes of the request.

Handling Edge Cases

AI quoting needs to handle complexity:

Custom Pricing Requests

async def handle_custom_request(requirements, standard_quote):
    """
    When prospect asks for pricing outside standard parameters
    """
    
    if requirements.get('custom_discount_request'):
        # Analyze if request is reasonable
        analysis = await claude.generate(f"""
        Analyze this custom pricing request:
        
        Standard quote: ${standard_quote['total_arr']}/year
        Requested discount: {requirements['custom_discount_request']}%
        
        Account context:
        - Company size: {requirements['company_employees']}
        - Industry: {requirements['industry']}
        - Competitors mentioned: {requirements.get('competitors', 'None')}
        
        Provide:
        1. Is this discount reasonable given context?
        2. Counter-offer suggestion if not
        3. Value-adds to offer instead of additional discount
        4. Approval recommendation
        """)
        
        return {
            'analysis': analysis,
            'requires_escalation': True,
            'suggested_response': generate_counter_offer(analysis)
        }

Multi-Product Bundles

def calculate_bundle_pricing(products, seats):
    """
    Intelligent bundling with optimal discount application
    """
    
    # Calculate standalone prices
    standalone_total = sum(
        get_product_price(p, seats) for p in products
    )
    
    # Check for bundle eligibility
    bundles = find_applicable_bundles(products)
    
    if bundles:
        best_bundle = max(bundles, key=lambda b: b['savings'])
        bundle_price = standalone_total * (1 - best_bundle['discount'])
        
        return {
            'pricing_method': 'bundle',
            'bundle_name': best_bundle['name'],
            'standalone_price': standalone_total,
            'bundle_price': bundle_price,
            'savings': standalone_total - bundle_price,
            'savings_percentage': best_bundle['discount'] * 100
        }
    
    return {
        'pricing_method': 'a_la_carte',
        'total_price': standalone_total
    }

Measuring Quote Automation Impact

Track these metrics:

Metric	Before AI	After AI	Impact
Quote turnaround	2-5 days	< 1 hour	95%+ faster
Quotes per rep/week	5-8	15-25	3x throughput
Quote accuracy	85%	99%+	Fewer revisions
Win rate	25%	32%	Faster = higher win
Average discount	28%	22%	Consistent enforcement

Implementation Roadmap

Week 1: Pricing Model

• Document all pricing tiers and rules
• Define discount thresholds and approvals
• Build pricing calculator with Claude Code

Week 2: Approval Workflow

• Map approval matrix by deal size/discount
• Build routing logic with Codex
• Test with edge cases

Week 3: Document Generation

• Create quote template
• Build PDF generation pipeline
• Test with sample quotes

Week 4: Integration & Launch

• Connect to CRM
• Deploy OpenClaw automation
• Train team on new process

The Speed Advantage

In B2B sales, speed is a competitive moat.

The company that delivers an accurate quote in 15 minutes isn't just more efficient—they're demonstrating that they understand urgency. They're showing what it's like to work with them.

Your competitors are still emailing spreadsheets back and forth. You're closing deals.

Build the advantage.

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