AI Dynamic Pricing Engine

Vietnam's motorbike resale market moves fast. Pricing a used vehicle accurately requires weighing brand, model, year, mileage, condition, local demand, and recent comparable sales — a process that was taking experienced staff up to 60 minutes per vehicle and still producing inconsistent results.

Golden Sea built a TensorFlow-based pricing model trained on 100,000+ historical transaction records from the client's own marketplace. The model learns pricing patterns across hundreds of make/model combinations, adjusting for current market conditions, seasonality, and regional demand signals in real time.

Input takes 5 minutes instead of 60: staff photograph the vehicle, enter basic specs, and the model returns a recommended list price with confidence interval and comparable recent sales. The recommendation isn't a black box — each output includes the top 5 factors influencing the price, so staff can understand and override when needed.

Post-deployment results: pricing time dropped from 60 minutes to under 5 minutes per vehicle. Average profit margin improved 30% due to tighter price calibration — fewer vehicles priced below market value, fewer sitting unsold due to overpricing. The ROI on the project paid back in under 90 days.