Car Selection Assistant

Problem

Car selection is not solved by a one-line request like “which SUV under 200k?” Buyers need to compare budget, usage, household space, energy restrictions, brand preferences, must-have features, and real on-road price differences. Meanwhile, trims, discounts, owner feedback, and crash-test data change quickly, so relying on outdated experience can surface discontinued models or incorrect configurations.

How It Works

Car Selection Assistant starts by collecting required inputs: budget range, usage scenario, and household structure, then fills optional preferences such as energy type, vehicle class, brand tendency, must-have features, exclusions, and city of purchase. If the request is too thin, it makes reasonable assumptions and marks them in the report.

The workflow then runs in fixed stages:
- Model screening: searches current or upcoming models online and covers different positioning levels;
- Data research: gathers specs, trim configurations, MSRP, estimated on-road price, fuel economy or range, crash-test results, and owner feedback;
- Report generation: produces an interactive HTML report with requirement profile, model cards, spec table, configuration matrix, radar chart, pros and cons, safety assessment, owner voice, and final recommendation.

Boundaries

It is useful for domestic car-purchase comparisons, especially restricted-plate cities, EV preference, family space needs, or trim trade-offs. The report presents pros and cons side by side instead of acting as a sales pitch; however, regional pricing, plate policy, and dealer inventory can still change, so final quotes should be verified locally.