RepairStack's ML pricing engine learns from your shop's history to find the maximum price your customer will pay and walk away happy. Not guesswork. Data.
The engine ingests your complete job history. Every quote, every invoice, every outcome. Your data becomes the foundation.
HP, repair type, motor subtype, customer history, seasonality, complexity signals, and more. The model sees patterns humans miss.
For every job, the engine identifies the price your customer will accept. Not the average. The maximum where they still feel good about the deal.
The model retrains every Sunday on fresh data. As your shop evolves and market conditions shift, the pricing stays current.
Most pricing tools just average your past quotes. That is the wrong target.
If you have been underpricing 100HP rewinds for three years, the average of those quotes is still too low. It is just the average of your mistakes.
RepairStack's engine identifies where the best outcomes happened: the jobs that were priced well, completed profitably, and kept the customer coming back. Then it targets that ceiling for every new quote.
Result: a 100HP rewind went from $1,748 to $4,814. The historical median was $4,700.
The engine provides pricing estimates for 82% of all incoming jobs automatically.
Every estimate considers motor specs, repair complexity, customer patterns, and market conditions.
Each estimate includes a confidence score so your team knows when to trust it and when to apply judgment.
The interactive demo includes the pricing engine. Give it a motor and watch it work.