AI-Powered Quote Estimator Based on Property Size for a Cleaning Services Startup

Overview

Built a machine learning-based quote estimator for an early-stage cleaning services startup, trained on the founder's historical job data to predict accurate pricing based on property size, layout, and requested service level. This let the startup offer instant, consistent online quotes from day one rather than the founder manually pricing every request based on gut feeling, which didn't scale as inquiry volume grew. The model was intentionally kept simple and interpretable so the founder could understand and adjust its pricing logic as the business's costs changed. It was built to retrain periodically as more completed jobs added real pricing data to learn from. An early-stage cleaning services startup relied on the founder manually pricing every request based on gut feeling, which didn't scale as inquiry volume grew and produced inconsistent quotes. We built a machine learning-based quote estimator trained on the founder's historical job data, predicting accurate pricing from property size, layout, and requested service level — kept simple and interpretable so the founder could adjust its logic as costs changed. We started with the limited historical job data the founder already had, deliberately choosing an interpretable model type over a black-box approach given the small dataset. The estimator launched with the founder still spot-checking quotes for the first few weeks before letting it run fully unsupervised. The startup now offers consistent, defensible pricing from its very first customers instead of ad hoc guesswork, with the model improving as more completed jobs feed back into it.