ecommerce_ops · manufacturing · workflow
From Filters to Phrases: AI-powered natural language car search at coches.net
Users of coches.net had to navigate through multiple filters to find specific cars, as the system required knowledge of which filters to apply rather than supporting natural language queries.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits natural language query
A user types a natural language phrase such as 'family-friendly SUV for mountain trips' or 'sporty convertible under €30,000 in Madrid' into the search bar.
Tools used
Amazon BedrockClaudeHaikuFew-shot prompting
Outcome
The AI search was deployed to production at coches.net with around 20,000 model invocations per day at a cost of about €19 per day. Users leveraging AI search generated more value than those using traditional filters, though adoption remained limited.
Results
VolumeAround 20,000
Cost replacedabout €19 per day
Grounding & classification
Source type: technical build writeup
25 fields verified against source quotes, 1 dropped as unverifiable.
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