Sherpa Auto Transport

Transformed Sherpa Auto Transport

How Cplus Soft’s AI-Driven Cost Prediction Engine Transformed Sherpa Auto Transport’s Quoting Process

Industry

Logistics

Region

USA

Duration

6 Months

Overview

Sherpa Auto Transport, founded in 2017, is a nationwide auto-transport brokerage specialising in vehicle shipments across the United States (including Alaska and Hawaii) with additional international services. SAT’s core objective was to integrate AI capabilities into its pricing process in order to deliver fair, transparent quotes, minimise inefficiencies and unpredictability in quoting shipping rates, and strengthen customer trust.

Challenges Faced by Sherpa Auto Transport

As the company scaled rapidly like managing over 100,000 vehicle shipments, its legacy quoting process showed critical limitations. Manual quoting and carrier cost fluctuations resulted in time-consuming calculations, unpredictable costs for customers, a high risk of disputes, and a lack of scalability. These inefficiencies undermined Sherpa’s promise of transparency and made it harder to maintain consistent margins and operational speed.

Moreover, the existing workflow lacked real-time automation and predictive capabilities. Without a system to dynamically estimate costs based on vehicle data and carrier variables, Sherpa’s support team faced high workloads and slower quote delivery, reducing conversion rates and limiting growth potential. The company needed a robust digital platform powered by automation and AI in order to meet customer expectations and operational demands.

Solution Provided by Cplus Soft

Cplus Soft partnered with Sherpa to develop a custom AI-powered cost-prediction engine that automates carrier cost estimation from point A to point B. The system ingests vehicle specifications (model, variant, weight, year of manufacturing, unique car DNA) along with historical carrier cost data, shipping variables, and route information to generate highly accurate cost predictions in real time. This shift replaced manual quoting with an automated, data-driven process.

The solution was seamlessly integrated into Sherpa’s quoting system, offering instant cost estimates via RESTful APIs and enabling customer-facing transparency. The infrastructure leveraged Python/Node.js, PostgreSQL/MongoDB, AWS Cloud and Docker to deliver scalability, speed and accuracy. As a result, Sherpa was able to deliver instant quotes, reduce manual workload, enhance conversion rates and strengthen pricing consistency and customer trust.

Key Features Implemented

AI Algorithm

Machine-learning model trained on historical pricing and carrier data for accurate cost predictions.

Vehicle Data Input

Model, variant, weight, year of manufacture and unique “car DNA” fields for granular cost estimation.

Real-Time Calculations

Instant cost estimates from manufacturer to buyer (point A → point B).

System Integration

Seamless connection with Sherpa’s quoting system for customer-facing use via RESTful APIs.

Scalable Infrastructure

Deployed on AWS Cloud with Docker for high-volume, real-time processing.

Data Collection Methods for Sherpa

Surveys

Collected customer feedback on quoting experience and pricing transparency.

Interviews

Conducted with logistics staff, carriers and customers to map pain points and cost variation drivers.

Focus Groups

Engaged internal teams (quoting, operations, customer support) to capture workflow inefficiencies.

Observations

Monitored quoting workflows to identify manual bottlenecks and error rates.

Document Reviews

Analysed historical cost records, carrier contracts and prior quote discrepancies.

Data Analytics

Applied analytics on historical shipments to detect cost patterns, variability and pricing errors.

Feedback Forms

Post-implementation feedback collected from customers and staff to validate improvements.

Case Studies

Reviewed similar AI-enabled pricing implementations in logistics to benchmark performance and design.

Setting Up Reporting Frameworks for Sherpa

Define Goals

Clear objectives were set such as improving quoting speed, accuracy, reducing disputes, and boosting conversion.

Select Metrics

KPIs selected included auto-quote rate, manual workload reduction, quote turnaround time, pricing dispute rate and conversion rate.

Determine Frequency

Weekly and monthly reports were established to monitor performance and trends.

Assign Responsibilities

Team members designated to manage data collection (logistics team), analytics (data team), and reporting (operations leadership).

Choose Tools

Tools such as Google Analytics, an internal dashboard and data-warehouse reporting system were used for tracking.

Review & Adjust

Regular review meetings were held to refine the AI model, workflow integration and user interface to further optimize quoting performance.

Implement Process

Structured workflows for data ingestion, model performance review, and continuous improvement were defined.

Impacts after Implementation

15% boost in customer satisfaction scores.

25% reduction in manual workload for quoting operations.

95% faster quoting process, with predictions delivered instantly.

20% improvement in conversion rates due to faster, transparent quoting.

30% reduction in pricing disputes thanks to AI-driven consistency.

Ubaidullah

Ubaidullah

Ubaidullah

Ubaidullah

Transformed Sherpa Auto Transport

How Cplus Soft’s AI-Driven Cost Prediction Engine Transformed Sherpa Auto Transport’s Quoting Process

Industry

Region

Duration

Overview

Challenges Faced by Sherpa Auto Transport

Solution Provided by Cplus Soft

Key Features Implemented

Data Collection Methods for Sherpa

Surveys

Interviews

Focus Groups

Observations

Document Reviews

Data Analytics

Feedback Forms

Case Studies

Setting Up Reporting Frameworks for Sherpa

Impacts after Implementation