Conversational product discovery for a global automotive catalog.

The situation #

A global automotive manufacturer had its digital presence scattered across dozens of market-specific microsites that didn't share infrastructure. Each region had built its own. Product data was duplicated, drifted out of sync between markets, and customers comparing models across pages were often comparing different versions of the same vehicle.

What we found #

The visible problem was inconsistency. The real problem was that no customer could answer "which one should I buy" from the spec tables they were being shown. Horsepower and trim level don't map to how people actually choose a vehicle, and the existing UI was forcing them into that frame anyway. Cleaning up the content infrastructure would fix the symptom. It wouldn't fix the buying experience.

What we built #

Two layers. The first was the platform itself: a modular content architecture with a single source of truth for the product catalog, region-specific microsites that pulled from it, and a publishing model that let local teams configure without forking the codebase.

The second was an assistant that sat on top. Customers could describe what they actually wanted in natural language, like a daily driver that handles winter roads, or something that fits a growing family. The assistant mapped those inputs against the catalog and surfaced the right product line, then handed off to dealer locators and configurators. This was early NLP work, pre-LLM, built around intent classification and structured mapping. It wasn't a chatbot. It was wired into the actual sales funnel.

What changed #

Regional teams shipped market-specific content without re-architecting every release. The product catalog became one place, not dozens of drifting copies. Most importantly, the assistant routed traffic into the buy-cycle directly, which meant the AI investment touched the funnel instead of just the marketing site.

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