Most of the big automotive data providers already structure specs in a way you can use, but they stop at raw attributes, not shopper friendly summaries, so you still need a layer that maps spec changes into meaningful differences like safety upgrades, infotainment changes, or powertrain improvements. The workable approach is to normalize the data feed, define rules that flag material changes between model years, then generate short templated summaries instead of trying to rely on open ended AI writing. AI can help phrase it, but the real value comes from clean data mapping and business logic that decides what actually matters to a buyer.
