Natural-language search over Zillow

MEDIA PREVIEW

Overview & Result

Jev can serve as better natural language search on websites. It can scan thousands of Zillow listings and classify properties by things you can't normally filter for - e.g. architecture, renovation status, proximity to freeways. This was done in <20 sec and costs $0.18 👇

How Jev fits in the loop

1. Ingest real-time application state and relevant contextual parameters
2. Format the decision problem as a bounded Choice or Noul schema
3. Query Jev to receive a typed probability distribution in sub-50ms
4. Execute downstream actions or route tasks according to the winning choice

How to reproduce

1. Inspect the original showcase and source material at https://x.com/venturetwins/status/2101341075684434245
2. Verify the bounded prompt and input candidate schema configured for Jev
3. Benchmark decision latency and classification accuracy against baseline models

Why this build matters

Demonstrates a real-world, cost-effective implementation of Jev in a search & content classification scenario, replacing expensive generative calls with fast typed decisions.

Reported performance

Latency: Sub-50ms deterministic decision window

Limitations

• Task accuracy is bounded by the precision of the defined candidate choices
• Third-party external dependencies and network latency may affect total workflow duration

Patterns