Contact person: Business Client

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Location: Bari, Italy

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I’m building a smart search engine that helps house-hunters discover residential listings they’ll actually want to visit. The core of the project is an AI-driven recommendation model that filters and ranks properties by two key signals—location and property type—and then serves up tailored suggestions in real time.

Here’s the flow I’m after: users enter a preferred area (city, neighborhood, or ZIP) plus the style of home they’re after (condo, single-family, townhouse, etc.). Your system ingests current listings, interprets those inputs, and returns a short list of the best-fit matches along with a confidence score that explains why each property made the cut. Accuracy and speed are more important to me than covering every possible filter; I can add price ranges and other criteria later once the core engine is rock solid.

I’m language-agnostic as long as the model is explainable and delivered with clean, well-commented code. If you lean on Python, Scikit-learn, TensorFlow, or similar libraries, great—just outline why they’re the right fit.

Deliverables I need to sign off on:
• A trained recommendation engine focused on residential data
• An API (or lightweight web interface) that lets me test queries by location and property type
• Clear documentation covering setup, data sources, model logic, and future extension points
• A short demo video or live walkthrough showing the AI in action

Once the prototype is stable, I’ll handle front-end polish and extra filters, so keep the first release laser-focused on accurate residential search and recommendations. Let me know your preferred tech stack and timeline, and we can get started." (client-provided description)