**Abstract.** The Tenders & Proposals team at an anonymized industrial partner in the e-mobility sector plays a key role in growing the business by answering presales requests. One of the main challenges they face is the absence of a structured way to prioritize those requests. Decisions are mostly based on individual experience, which can result in time spent on low-impact opportunities while higher-value ones are missed. To support the team, we created a decision-support tool powered by machine learning to prioritize quotes based on data rather than intuition. The baseline model is a Random Forest (RF), trained on past quotes and variables such as complexity level, price tier, urgency (Days Until Due = Due Date − Request Date), and total value. Features were cleaned, recategorized, and grouped when needed—for example, total value was grouped by business rule (0–$5M vs $5M–$25M). On top of that, a priority score was built from business rules. To improve detection of “Won” opportunities, we evaluated a Gradient Boosting (GB) variant and cross-validation. In offline tests, the RF baseline achieved 90.0% accuracy (weighted F1 0.89); a GB variant increased “Won” precision to 50.0% (recall 13.3%). *Preprint — not peer-reviewed. Version 1.0 (2025-09-17).*