Quote Generation Agent

The work it takes off your plate

Sales teams face slow and error-prone quoting processes because key details are scattered across diverse systems and documents. Manual effort to piece together account data, purchase orders and pricing rules often results in delays, inconsistencies and substandard outputs. Discount policies are not applied uniformly, creating margin risks and compliance issues. Approvals add further bottlenecks, while the lack of standardization makes it difficult to scale as volumes grow. Together, these challenges erode customer trust, delay revenue and overburden sales operations. Quote Generation Agent unifies customer data across Salesforce CRM, purchase orders and knowledge base insights into a single, structured profile. Powered by LLMs, it applies discount rules transparently, generates clear discount rationales and adds tailored upsell and cross-sell recommendations. Exceptions are flagged and routed for approval, while validated quotes are formatted into polished, professional PDFs and stored in Salesforce for full traceability. By automating the complete process, the agent standardizes quoting practices, ensures policy compliance, improves accuracy, and delivers consistent, high-quality quotes that enhance customer trust.

How it works

quote generation agent automates the end-to-end workflow of creating accurate, compliant and customer-ready sales quotations. It combines Salesforce CRM data, purchase order details, knowledge base insights, pricing policies and LLM-driven reasoning to generate structured and professional quotes. The workflow of the agent is defined by the following steps:

1. Step 1: Comprehensive Data Extraction and Structured Synthesis

   • The agent retrieves account, contact, opportunity, product, price book, and sales-owner data from the connected CRM.
   • It extracts line items, quantities, requested delivery dates, billing details, and payment requirements from purchase orders and supporting documents.
   • It reconciles customer, product, and currency identifiers across source systems and flags conflicting values for review.
   • It assembles a structured quote profile containing customer requirements, commercial context, and required output fields.

   Result: A validated, unified quote profile is prepared for policy evaluation and quotation drafting.

2. Step 2: Knowledge Base Search

   • The agent searches approved knowledge sources for current product descriptions, pricing guidance, contract terms, tax rules, and quote templates.
   • It retrieves applicable discount thresholds, customer-specific terms, renewal conditions, and required legal language.
   • It ranks supporting content by relevance and effective date so superseded policies are not used.
   • It records source references and evidence for each material pricing or terms decision.

   Result: The quote profile is enriched with current, traceable commercial and policy guidance.

3. Step 3: Upsell and Cross-sell Recommendations

   • The agent compares purchased products with compatible add-ons, service packages, upgrades, and complementary offerings.
   • It uses account industry, installed products, usage indicators, opportunity context, and knowledge-base rules to rank recommendations.
   • It excludes incompatible, unavailable, duplicate, or contract-restricted products from the recommendation set.
   • It generates concise business rationales that explain the customer value and commercial relevance of each recommendation.

   Result: The draft quote includes prioritized, customer-relevant expansion options with clear supporting rationales.

4. Step 4: Pricing, Discount Policy Application, and Approval Handling

   • The agent calculates list prices, negotiated prices, discounts, taxes, fees, subtotals, and final totals using the applicable currency and price book.
   • It applies customer-tier, volume, term, and promotion rules while recording the rule and evidence behind each discount.
   • It compares the proposed quote against approval thresholds, margin floors, credit terms, and exception policies.
   • It routes out-of-policy discounts, unusual terms, or missing approvals to designated approvers and pauses finalization until resolution.

   Result: A policy-compliant quote is calculated with transparent discount reasoning and documented approval status.

5. Step 5: Final Quotation Generation

   • The agent populates the approved quote template with customer information, line items, recommendations, pricing rationale, terms, validity dates, and signatures.
   • It validates arithmetic, required fields, product availability, tax treatment, and consistency between the quote and source records.
   • It renders the quotation as a professional PDF with a unique quote identifier and version history.
   • It stores the final document, source references, approval trail, and delivery status in the system of record.

   Result: A customer-ready PDF quotation is delivered and archived with complete traceability.

Why use it

• Faster quote turnaround. Automates end-to-end quote creation, reducing preparation time and enabling quick customer responses.
• Error reduction. Eliminates manual operations and fragmented document handling, producing reliable quotes based on real-time data.
• Shortened sales cycle. Faster turnaround on quotations helps organizations reduce delays and move deals to closure more efficiently.
• Improved customer trust and loyalty. Delivering timely, professional and transparent quotations builds credibility, enhances the buying experience and builds long-term customer relationships.
• Competitive advantage. By responding faster with accurate and tailored proposals, sales teams can meet customer expectations and compete effectively in high-stakes opportunities.
• Operational efficiency at scale. Supports growing sales pipelines without requiring proportional increases in resources.