Development of AI-Driven Price Estimation System for Construction Material Trading

The client operates within an unpredictable market where fluctuating prices of raw materials, currency exchange rates, and variable demand hinder accurate offer pricing. Traditional decision-making relies heavily on intuition, leading to inconsistent margins, decreased sales volume, and unprofitable projects due to lack of timely, data-driven insights. Moreover, limited access to real-time external market data impairs the ability to customize offers effectively.

A mid to large-sized European construction materials trading company operating in a volatile market, seeking to optimize pricing and delivery processes.

• Implement an AI-powered predictive model to estimate total customer order value based on historical data, current market conditions, and order specifics.
• Reduce pricing margin errors, thereby decreasing discrepancies between estimated and actual payable amounts upon delivery.
• Enable the sales team with automated, accurate, and timely offer calculations to improve customer experience and boost sales volume.
• Automate the sourcing of relevant external market data through robotic process automation (RPA) to ensure up-to-date information for decision-making.
• Validate the feasibility of AI in dynamic market environments via rapid prototyping, achieving initial model accuracy within two weeks, followed by continuous model refinement.

• Order Data Export Module: Allows sales personnel to export order details into a standardized CSV format for processing.
• Data Integration Pipeline: Automates the retrieval of external market data (e.g., commodity prices, stock exchange listings) via RPA robots and internal data sources.
• Predictive Price Estimation Model: Uses machine learning algorithms to forecast the total payable amount considering order volume, resources, customer profiles, and external factors.
• Automated Workflow Trigger: Initiates data processing and prediction upon receipt of order files, employing email triggers and RPA robots for seamless integration.
• Result Delivery System: Sends back price estimates via email to the sales team with minimal manual intervention.
• Continuous Model Improvement: Incorporates feedback and new data to enhance prediction accuracy over time.

• External market data sources (e.g., commodity exchanges, currency rates APIs)
• Internal customer and order databases
• Email systems for trigger-based communication
• Data repositories for historical and real-time data storage

• System should process and generate price estimates within 30 seconds post data receipt
• Ensure data security and user access control around sensitive commercial data
• Scalable infrastructure to handle increasing volume of order data and external data sources
• High reliability with 99.9% system uptime for workflow automation

The deployment of the AI-based price estimation system is expected to significantly reduce margin of error in order pricing, leading to more accurate customer quotes and increased profit margins. It will support the sales team with real-time, data-driven insights, enabling faster decision-making and enhanced customer satisfaction. Over time, this will enhance market competitiveness, increase sales volume, and improve overall profitability through more precise and consistent pricing strategies.