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The Lead Qualification Scoring Agent automates the assessment of incoming inquiries by analyzing and scoring leads based on key criteria. Utilizing customizable scoring rules, the agent helps prioritize leads by their potential to convert, focusing efforts on the most promising prospects.

Challenges the Lead Qualification Scoring Agent Addresses:

Businesses often struggle with the wide variation in lead quality, leading to inefficiencies and misallocation of resources. Sales teams can waste time on low-value inquiries while high-potential leads are overlooked, resulting in delayed responses and missed revenue opportunities.

The Lead Qualification Scoring Agent streamlines lead management by automatically analyzing incoming inquiries and assigning scores based on the contact person’s decision power, company size, revenue, and geographical location. By filtering out low-value inquiries and emphasizing high-priority leads, this agent enables teams to efficiently focus their efforts on prospects with the highest conversion potential, enhancing productivity and boosting growth.

How The Agent Works:

The Lead Qualification Scoring Agent is designed to automate and streamline the entire lead qualification scoring workflow. The agent is activated when new lead data is received from the specified input sources it monitors (e.g., web forms, email inquiries), automatically initiating a series of predefined, streamlined steps to assess and qualify the lead. Leveraging an advanced large language model (LLM), the agent analyzes lead data in real-time, evaluating key attributes to make data-driven decisions and execute the necessary actions, ensuring precision and efficiency at every stage of the lead qualification process. Below is a detailed breakdown of how the agent works at each step of the process:

Step 1: Lead Data Capture and Analysis

Upon capturing new lead data through web forms, emails, or CRM systems, the agent begins the process of analyzing and evaluating the information to assess each lead's potential. The LLM evaluates the lead data to assess the nature of the inquiry, validate the prospect's domain and analyze spam leads. This step involves gathering basic information from various channels and enhancing it for accuracy and depth. The process involves several tasks:

Key Tasks

• Gather Lead’s Initial Data: The agent collects basic information such as name, email, phone number, organization details, and project needs from various sources such as web forms, emails, and CRM entries. This forms the base of the lead profile.
• Structure Lead Information: The agent then organizes and structures the collected data into a cohesive format, ensuring that all relevant details are categorized for easy processing and analysis.
• Initial Filtering: The agent first checks for spam by examining the nature of the inquiry, such as genuine project requests or any spam inquiries like marketing outreach or partnership requests. Any leads identified as spam are immediately rejected.
• Domain Verification: For valid leads, the agent extracts the domain from the provided email address to confirm its authenticity. For leads with personal emails, it performs a Google search using the organization's name to gather relevant information and validate the company.
• API Integration for Company Details:
  • After validating the domain, the agent integrates with APIs (e.g., Fresh LinkedIn Profile Data API) to collect key organization details, such as industry, company size, and location.
  • If company information is found, the agent calculates the company’s age based on its founding date to assess its experience and stability.
  • If further financial information is needed, a Google search is conducted to supplement the data.
• Managing Unverified Data: If no company data is found through APIs, the agent displays the message, "Unable to find organization information." The lead is then marked as unverified and deprioritized to ensure focus on verified leads.
• API Integration for Contact Details: If company details are found, the agent begins retrieving the contact person's information by performing a Google search to locate their LinkedIn profile. It then uses an API to fetch detailed information from LinkedIn, including the person's role, experience, and tenure at the current organization.

Outcome

• Consolidated Lead Insights: The agent compiles and refines lead data, providing a comprehensive evaluation that guides the scoring and qualification processes, ensuring accurate and effective utilization of lead information.

Step 2: Lead Scoring

Key Tasks:

• LLM-based Score Analysis: The agent submits the lead data to the LLM, which analyzes the information according to predefined rules. The LLM then calculates the lead score based on these established criteria.
• Apply Scoring Mechanism: The agent applies a scoring mechanism that assigns points based on the extracted information.
• Designation and Decision-making Power: Scores are assigned based on the position of the contact person, with higher points for C-level executives and other high-ranking decision-makers.
• Company Size and Revenue: The scoring mechanism considers company size and financial capacity, awarding higher scores to larger or financially stable companies.
• Lead Source and Target Market: Leads from preferred geographical regions and relevant industries receive higher scores.

Outcome:

• Priority Lead Identification: If the lead's total score surpasses a predetermined threshold, it is flagged as high-priority, signaling the sales team to initiate immediate engagement.

Step 3: Lead Scoring Report Generation

Key Task:

• Final Score Report Generation: The agent produces a comprehensive report for each lead, categorizing them based on multiple critical parameters such as urgency, company size, lead source, and contact person's job role and decision-making power.

Outcome:

• Lead Scoring Report: The report includes insights such as urgency level, potential project timeline, and any previous relationships with the company.
• Lead Prioritization: The report allows the sales team to prioritize high-quality leads for immediate follow-up while identifying lower-scoring leads for potential requalification or dismissal.

Step 4: Continuous Improvement Through Human Feedback

Key Tasks:

• Feedback Processing: Sales representatives provide feedback on the accuracy and relevance of the lead scoring.
• Error Correction: The feedback may highlight discrepancies or inaccuracies in the scoring process, which the agent uses to adjust its algorithms and parameters.

Outcome:

• Continuous Improvement: The agent evolves with each set of leads processed, becoming more accurate and efficient over time.

Why Use the Lead Qualification Scoring Agent?

• Enhanced Lead Prioritization: By scoring leads based on their potential value and relevance, the agent allows teams to focus on high-priority prospects.
• Time Efficiency: Automated lead analysis saves time by quickly discarding spam and low-quality leads.
• Customization and Flexibility: The rules and criteria used in scoring can be customized to align with the specific priorities of any organization, making it adaptable across industries.
• Improved Conversion Potential: By focusing on high-scoring leads, businesses can improve conversion rates and better allocate their resources toward valuable opportunities.

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Many organizations struggle with outdated, incomplete, or poorly organized sales documentation, including case studies, technical specifications, product overviews, and proposal templates. These challenges slow down proposal turnaround times, create inconsistencies in messaging, and risk lost opportunities due to inadequate materials. The agent addresses these challenges by analyzing prospect requirements and cross-referencing them against the organization’s documentation repository. Using LLM-powered search and categorization, it identifies content gaps and recommends the creation of targeted assets. It also aligns resources with prospect pain points and industry context, increasing the relevance and impact of every sales interaction. By maintaining a continuously evolving library of sales collateral, the agent accelerates proposal delivery, strengthens alignment between marketing, product, and sales teams, and ensures consistent, high-quality communication. The result is stronger client engagement, shorter deal cycles, and a more adaptive sales enablement strategy that evolves with customer needs. [image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/ticket-escalation-recommendation-agent.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/ticket-escalation-recommendation-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Sales [subDepartment] => Sales Operations [process] => Sales Collateral Management [subtitle] => Recommends the most relevant sales collateral by matching prospect needs with curated resources, ensuring faster, consistent, and impactful engagements. [route] => sales-collateral-recommendation-agent [addedOn] => 1755763165841 [modifiedOn] => 1755763165841 ) [2] => Array ( [_id] => 68a468d9cea69771f8b38bbf [name] => Sales Performance Analyzer Agent [description] => ZBrain Sales Performance Analyzer Agent is designed to measure and enhance sales effectiveness across individuals and territories. Many organizations struggle with fragmented sales data spread across multiple systems, making it difficult to track performance, identify skill gaps, and evaluate market coverage. This lack of visibility often results in missed opportunities, inefficient resource allocation, and slower growth. The agent addresses these challenges by consolidating data from CRM systems, deal pipelines, and activity logs into a unified performance view. It applies advanced analytics to track KPIs such as closure rates, lead-to-deal conversion ratios, revenue contribution, and territory coverage. By benchmarking performance across sales representatives and regions, it reveals patterns, highlights strengths, and pinpoints underperforming areas that need attention. With structured insights at hand, organizations can make smarter strategic decisions, optimize territory assignments, and deliver targeted training programs. Sales leaders gain the ability to identify top performers, close skill gaps more quickly, and allocate resources with greater precision. [image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/supplier-performance-monitoring-agent.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/supplier-performance-monitoring-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Sales [subDepartment] => Sales Operations [process] => Sales Performance Management [subtitle] => Analyzes sales performance across representatives and territories, delivering actionable insights to optimize strategies and accelerate growth. [route] => sales-performance-analyzer-agent [addedOn] => 1755605209718 [modifiedOn] => 1755605209718 ) [3] => Array ( [_id] => 682c10d0e8ab854cb579cf0e [name] => Dynamic Deal Documentation Agent [description] => Dynamic Deal Documentation Agent, developed by ZBrain, is an automation-driven solution built to streamline the creation and management of deal-related documents across sales and legal operations. In fast-paced enterprise environments where contracts, proposals, and agreements must be generated quickly and accurately, this agent ensures that documentation keeps pace with deal progression. It connects sales workflows with document generation in real time—reducing turnaround time and ensuring consistency across every customer-facing asset.

The agent integrates with CRM platforms to retrieve real-time deal data such as client details, commercial terms, product configurations, and pricing. Using predefined, role-specific templates, it automatically generates tailored documents that reflect the most current information without requiring manual entry or formatting. Every document—whether it’s a proposal, service agreement, or contract—is dynamically populated with deal-specific variables, maintaining both accuracy and compliance with internal standards. It also supports version control and centralized tracking, ensuring documents remain aligned with the latest deal status.

By automating the document lifecycle from creation to completion, the Dynamic Deal Documentation Agent enhances operational efficiency and reduces risk across sales and legal functions. It shortens the time required to move deals forward, minimizes human errors, and ensures that teams are working from a single, trusted source of truth. The result is a more responsive and scalable documentation process that supports faster deal closures, improved governance, and greater alignment between sales execution and business compliance.

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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.

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At the core the agent monitors CRM systems for confirmed sales outcomes. Once a deal is closed, the agent extracts the relevant data, formats it according to OMS specifications, and executes a series of pre-submission validations. These include checks for data completeness, product and pricing accuracy, contract and payment alignment, and consistency with existing customer records. The system leverages configurable business logic, rule-based exception handling, and API-driven integrations to ensure compatibility with a wide range of enterprise platforms and data models. The result is a fully automated, standards-compliant sales order that minimizes the risk of fulfillment delays or costly downstream corrections.

To further enhance resilience and adaptability, the Sales Order Creation and Validation Agent incorporates a human feedback loop, enabling sales operations and finance teams to review, correct, and annotate flagged exceptions. This feedback informs continuous improvement in the agent’s rule engine and validation routines. As a result, organizations benefit from faster order cycles, fewer manual interventions, and stronger alignment between commercial and operational functions. By enforcing structured, accurate, and compliant sales order creation, the agent serves as a vital enabler of operational efficiency and cross-functional reliability at scale.

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This agent is designed to seamlessly integrate with existing CRM systems, ensuring that the insights and answers it provides are not only relevant but also based on the most current data available. This integration makes it a reliable assistant, offering immediate access to critical sales information without disrupting existing workflows. By minimizing errors and enhancing efficiency, the CRM Insight Agent plays a pivotal role in streamlining sales operations. Feedback from users is continuously integrated into its functionality, ensuring ongoing improvements and alignment with the evolving needs of sales teams. With this tool, sales professionals are better equipped to maximize their efforts, ultimately leading to improved sales performance and customer satisfaction.

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Dynamic Deal Documentation Agent

The Dynamic Documentation Agent automates the creation of deal documents by pulling data from a CRM, populating templates, and generating accurate contracts, proposals, and agreements with minimal manual input.

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Quote Generation Agent

Automates quote generation, applies pricing rules, and ensures approval workflows for consistent, profitable sales deals.

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Sales Order Creation and Validation Agent

Automatically creates and validates sales orders in the Order Management Systems by monitoring CRM for finalized deals, ensuring completeness, accuracy, and compliance.

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CRM Insight Agent

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