Quote Generation Agent

ZBrain Quote Generation Agent automates the creation of accurate, compliant and professional sales quotations, removing delays and inconsistencies of manual preparation. By integrating with Salesforce and enriching inputs with knowledge base insights, the agent consolidates scattered data into a single, reliable source. Powered by LLMs, it applies pricing policies transparently and generates polished, customer-ready quotes in minutes. This accelerates deal cycles, protects margins and strengthens customer trust with consistent, high-quality quotations at scale.

Challenges the Quote Generation Agent Addresses

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.

ZBrain 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 the Agent Works

ZBrain 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:

Step 1: Comprehensive Data Extraction and Structured Synthesis

The workflow begins when a user submits an account name through the agent dashboard.

Key tasks:

• Salesforce account detail retrieval: The agent queries Salesforce CRM to fetch account-level details, including account ID and name, industry, type, number of employees, billing address, shipping address and description. This establishes the baseline profile of the customer.
• Salesforce opportunity detail retrieval: Executes a structured query to fetch opportunity-level details tied to the submitted account. Specifically, it retrieves the opportunity ID, type of engagement (new, renewal, upsell, etc.) and the requested final discount percentage. This ensures deal context and discount requests are captured upfront for downstream pricing logic.
• Purchase order extraction: The agent identifies the most recent purchase order attached to the opportunity, retrieves it using the linked document ID, generates a public link and download URL for reference, and extracts text from the PDF. The parsed content is then prepared for integration into the unified customer profile.
• LLM-driven data synthesis: The agent uses an LLM to compile the Salesforce account JSON, opportunity JSON and purchase order text into a single, normalized JSON profile. This unified data synthesis eliminates the need for toggling between multiple screens.
• Data validation: The agent verifies that critical attributes (such as account name, customer type, requested items and discount request) are present. If key data is missing, the workflow halts to prevent incomplete or inaccurate quotes.

Outcome:

• Comprehensive account profile: A unified and consistent customer profile is created, forming the foundation for pricing analysis, compliance checks, and quote generation.

Step 2: Knowledge Base Search

The agent enriches the structured profile by querying the connected knowledge base for deal-related context and historical intelligence.

Key tasks:

• Attribute retrieval: Captures structured inputs such as industry, company size, requested plan, user count, add-ons and requested discount.
• Contextual intelligence: References past deal outcomes, discount levels, adoption patterns, product catalog details, pricing tiers and upsell/cross-sell history.
• Structured integration: Normalizes all retrieved values into JSON for alignment with the unified profile created earlier.

Outcome:

• Contextual insights: Salesforce profiles are enriched with references from historical and product intelligence in the knowledge base, enabling more accurate pricing decisions and relevant sales recommendations downstream.

Step 3: Upsell and Cross-sell Recommendations

Using LLMs, the agent applies pricing policies, discount logic and sales recommendations based on customer type and organizational rules.

Key tasks:

• Upsell and cross-sell recommendations: The LLM analyzes the enriched profile to suggest higher-tier plans and complementary products, linking each suggestion to customer context and historical deal patterns.
• Pricing intelligence: The LLM explains discount rationales step by step, making reasoning transparent and audit-ready.

Outcome:

• Precise upsell and cross-sell recommendations: A structured set of upsell and cross-sell suggestions paired with clear, contextual discount rationales, forming a stronger basis for pricing validation and quote generation.

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

The agent applies appropriate discount rules, validates thresholds, and manages exception routing through integrated approval workflows before assembling a structured draft quotation.

Key tasks:

• Customer-type routing: Determines whether the customer is new or existing and applies the relevant pricing framework.
• Policy-driven discounts:
  • Existing customers: Sequential discounts are applied (loyalty, contract duration, add-ons, company size).
  • New customers: Discounts are capped at a fixed threshold, with safeguards in place.
• Threshold-based routing: If requested discounts surpass the policy-specific discount limit, the workflow branches to an approval path.
• Approval submission: The draft quotation, with customer context and discount rationale, is submitted into the Salesforce approval process.
• Manager notification: Relevant stakeholders are notified with a clear summary of the flagged discount exception.
• Calculation transparency: Generates step-by-step rationales for all applied rules to support auditability.
• Draft quotation assembly: Prepares a structured draft with sections such as customer snapshot, discount analysis, line-item breakdown, recommendations and pricing summary.

Outcome:

• Policy-validated draft quote: A draft quotation is generated with validated discounts, transparent rationales and approval escalations automatically routed where needed.

Step 5: Final Quotation Generation

In this step, the agent produces the professional, customer-ready quotation.

Key tasks:

• Pre-generation safety checks: Verifies fields such as total quoted price and approval status, halting with error messaging if anomalies occur.
• Structured formatting: The LLM maps all elements—products, quantities, unit prices, discounts and net totals—into a fixed, standardized format.
• Conversion, packaging and storage: Converts the draft to HTML, renders it into a PDF, and saves it as a Salesforce content version linked to the relevant account and opportunity records.

Outcome:

• Professional, audit-ready quotation: A finalized, approval-validated quotation is generated and stored in Salesforce, ensuring accuracy, transparency and consistency in every customer interaction.

Why use Quote Generation Agent?

• 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 fosters 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.