# Natural-Language Query Layer for Property Search | Super Cat

> Users describe the property in a sentence; the system resolves it into the structured query underneath — against a schema that has to return exact results.

Source: https://www.super-cat.tech/portfolio/nl-query-property-search  
Last modified: 2026-10-22

[← All case studies](https://www.super-cat.tech/portfolio)Rated 5.0 · shipped to a live product

# Natural-Language Query Layer for Property Search

Users describe the property in a sentence; the system resolves it into the structured query underneath — against a schema that has to return exact results.

## The problem

A property search platform had the data and it had the filters, but users still had to translate what they wanted into a filter panel before the system could help them. Every search started with the user doing the work of a query planner.

## What we built

A natural-language query layer on top of the existing schema: the user describes the property in a sentence, and the system resolves that into the structured query underneath. Delivered as a fixed-price engagement and rated 5.0.

Before: Users translate wants into filters; Search must return exact matches; A missed listing is a visible bug. After: Rated 5.0 · shipped to a live product.Before

- Users translate wants into filters

- Search must return exact matches

- A missed listing is a visible bug

After
Rated 5.0 · shipped to a live product

Stack
NL2SQL NLP FastAPI AI App

## Why it holds up

The interesting work is not the language model, it is the mapping between loose human phrasing and a schema that has to return exact results. Search is unforgiving in a way chat is not — a listing that should have matched and did not is a bug the user notices immediately, and there is no graceful hedge available.

Related service: [Hire vetted AI engineers](https://www.super-cat.tech/hire-vetted-ai-engineers)

## More case studies

Big 4 client · fully on-premise

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Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached.

- Juniors reading thousands of pages per question

- Seniors acting as search engines

Read the case → 200k calls/month · 67% booking rate vs 77% human

### 24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands

Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot.

- Night and weekend calls unanswered

- Staffing every shift hard to justify

Read the case → US$1M collected in the first 3 months

### AI Payment Collection and Reminders

The companion product to the AI front desk: it chases unpaid invoices with polite, timed reminders and a payment link, and stops the moment the customer pays or replies. It collected over US$1 million for the client in its first three months.

- Chasing payment is awkward and late

- Billable time lost to collections

Read the case →

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