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Case Study - Gen AI

AI-Powered Customer Quote Generation Automation

Case Study - Gen AI

Agentic AI for Dynamic Pricing in Phase 1 Clinical Trials for Vaccine Development

AI-Powered Sales Quote Automation Services

AI & GenAI Solutions

A major U.S.-based Energy Company was facing operational challenges due to inefficient and outdated wind power forecasting methods. Their internal process relied heavily on manual analysis of production charts, making it:A major U.S.-based Energy Company was facing operational challenges due to inefficient and outdated wind power forecasting methods. Their internal process relied heavily on manual analysis of production charts, making it

The Challenge

A leading life sciences organization conducting Phase 1 vaccine clinical trials faced significant challenges in managing study pricing and resource allocation. Clinical trial costs were influenced by multiple variables, including participant enrollment rates, site availability, resource utilization, protocol changes, geographic considerations, and regulatory requirements. Traditional pricing models relied heavily on historical assumptions and manual analysis, making it difficult to adapt to changing trial conditions in real time.

As a result, the organization struggled with:

Static pricing models that failed to reflect real-time trial dynamics

Delays in pricing adjustments due to manual analysis

Inefficient resource allocation across trial sites

Higher costs from inaccurate demand forecasting error

Limited visibility into factors impacting trial profitability

These challenges affected budget planning, operational efficiency, and the organization’s ability to optimize clinical trial investments.

What JBS Built

Jade Business Services developed an Agentic AI-powered Dynamic Pricing Platform designed specifically for Phase 1 clinical trial environments. The solution continuously analyzes multiple operational and financial variables and automatically recommends optimized pricing strategies based on real-time trial conditions. Instead of relying on static spreadsheets and periodic reviews, the platform leverages specialized AI agents that monitor trial performance, evaluate pricing scenarios, and provide intelligent recommendations to decision-makers.

The Challenge

Manual RFQ Processing Delaying Sales Cycles

A Fortune 200 manufacturer struggled with a manual quote generation process, slowing down RFQ responses and harming customer experience. Inside Sales Engineers were overwhelmed by repetitive tasks, leaving little time for strategic engagement. Core issues included:

Inefficient RFQ data extraction

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Inconsistent quotation formats

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Inability to scale during peak demand

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Missed sales opportunities due to delayed responses

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Business Impact

Lost Revenue and Decreased Sales Productivity

These inefficiencies resulted in:

Delayed quote turnaround times and lower conversion rates and lower conversion rates

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Inconsistent customer experiences affecting brand trust

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Sales team burnout from manual quoting workloads

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Reduced operational scalability and slower sales growth

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How the Solution Works

RFQ Intake & Document Processing

Users upload RFQ documents through a simple web interface.
The platform automatically processes structured and unstructured documents, including customer requirements, product specifications, delivery terms, and pricing information.

AI-Powered Information Extraction

Specialized LLM-based agents analyze RFQ documents and identify:

Product SKUs

Customer requirements

Pricing parameters

Delivery conditions

Commercial terms

These inefficiencies directly impacted sales productivity, customer experience, and revenue
growth.

Intelligent Quote Generation

The platform orchestrates multiple AI agents, each responsible for a specific task:

Pricing Calculator Agent

Calculates product pricing

Applies discount rules

Performs pricing validations

Document Generation Agent

Creates standardized customer-ready quotations

Applies discount rules

Generates final quote documents automatically

Quote-to-Cash Integration

The solution integrates seamlessly with existing business systems, including:

ERP platforms

CRM applications

Quotation management systems

This enables automated approval workflows, pricing governance, and quote tracking across the organization.

Scalable RFQ Processing

The platform supports parallel processing of multiple RFQs simultaneously.
Advanced Contract Negotiation Pricing (CNP) logic and real-time discount validation ensure that every quote aligns with business policies while maintaining speed and accuracy.

The Solution

How the Solution Works

Multi-Source Data Collection

The platform ingests data from multiple clinical trial systems to create a centralized view of all variables impacting trial costs and pricing decisions, including:

  • Trial management platforms
  • Site performance systems
  • Resource planning tools
  • Financial and operational databases

AI-Driven Data Analysis

The platform leverages Agentic AI to identify patterns and relationships that are difficult to detect through manual analysis by continuously evaluating critical factors such as:

  • Participant enrollment trends
  • Site utilization rates
  • Resource availability
  • Operational costs & Trial timelines
  • Geographic and regulatory variables

Dynamic Pricing Intelligence

Specialized AI agents analyze current trial conditions to generate optimized pricing recommendations, enabling stakeholders to make faster and more informed pricing decisions.

  • Trial profitability
  • Resource utilization
  • Budget efficiency
  • Operational risk

Real-Time Decision Support

The platform continuously monitors trial performance and automatically adjusts recommendations as conditions change.

Decision-makers gain access to actionable insights through intuitive dashboards, enabling proactive responses to enrollment fluctuations, resource constraints, and cost variations.

Continuous Optimization

As additional data becomes available throughout the trial lifecycle, the AI agents refine their recommendations and improve forecasting accuracy.

This creates a continuously learning system capable of adapting to evolving clinical trial environments.

Business Outcomes

01

Improved Pricing Accuracy

The AI-driven pricing engine enabled more accurate pricing recommendations by considering real-time operational variables instead of relying solely on historical assumptions.

03

Optimized Resource Utilization

The platform improved visibility into resource allocation, helping teams maximize site and operational efficiency.

02

Faster Decision-Making

Automated analysis significantly reduced the time required to evaluate pricing scenarios and generate actionable recommendations.

04

Better Financial Planning

Real-time forecasting and dynamic pricing recommendations enabled more effective budget management and cost control throughout the trial lifecycle.

05

Increased Operational Agility

The organization gained the ability to respond quickly to changing trial conditions, reducing risk and improving overall operational performance.

Technology Stack

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Technical Depth with Delivery Discipline

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Cloud-Agnostic Execution

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Embedded Governance

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Deep Industry Expertise

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Faster Time-to-Value

CASE STUDIES

Real Transformation. Measurable Results.

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    Business Value Delivered

    By implementing an Agentic AI-powered Dynamic Pricing Platform, Jade Business Services transformed traditional clinical trial pricing into an intelligent, data-driven decision-making process.

    The solution enabled real-time pricing optimization, improved forecasting accuracy, enhanced resource utilization, and provided stakeholders with actionable insights to maximize the efficiency and profitability of Phase 1 vaccine clinical trials.