Commodities Firm Optimizes Bid Prices on High-Volume Transactions With Machine Learning

Pythian and the customer used historical records of accepted bid prices and synthetically generated prices for rejected bids to create a proof of concept (PoC) model that predicts the probability of a given customer accepting or not accepting contracts, depending on the proposed price. The proof of concept model focused on a subset of the company’s products.

• Pythian first conducted a discovery and analysis phase to examine the quality of datasets used in the PoC and develop business rules for generating synthetic price data used in the predictive model. 
• Pythian next imported the datasets into BigQuery and built a baseline model. This step included developing feature transformations (such as BigQuery preprocessing), creating synthetic data, and verifying synthetic data methods. 
• A neural network model was then trained and improved through an iterative process that identified and designed architecture improvements and tuned the model for better performance.

Thanks to Pythian’s expertise in ML solutions and close relationship with Google, our SMEs, in collaboration with the agribusiness firm's internal team, developed a PoC that made sense for the company based on their understanding of the technology and the timelines and resources they needed to accommodate. 

• Google Cloud
• Google BigQuery
• Vertex AI
• TensorFlow