AI Real Estate Portal MVP
Budget: $1,500 – $3,000 USD
I’m kicking off an MVP for an AI-centric real-estate portal and need a developer—or a tight-knit team—to get the first version live right away.
What must work from day one
• On-demand image rendering: when an agent uploads a photo (or none at all), the system should call the OpenAI API and DALL·E to generate or enhance listing visuals in real time, store the assets, and return URLs to the client app.
• AI-based research: buyers and agents will type or speak questions and receive chat-style answers that draw on listing data, local price trends, and comparable sales. I expect embeddings, retrieval, and summarisation to reside in a small Python or Node.js service for now.
Features to line up next (scaffolding now, polish later) include voice control via Whisper, invisible text layers for fuzzy voice search, duplicate-listing detection, an apartment-scanner that extracts room dimensions from user photos, price/sales comparison widgets, and an interactive map. Structuring the code so these modules can be toggled on or off is important.
Tech notes
• Front-end: React (web) or Flutter (mobile).
• Back-end: Node.js or Python with a clean REST/GraphQL layer.
• AI stack: OpenAI API is confirmed; Whisper and DALL·E are likely follow-ups.
• Dev environment: something Replit/Bolt-like for easy previews, yet the repo must stay in my GitHub with undisputed code ownership.
Deliverables – Sprint 1
1. Working image-rendering microservice (Dockerised).
2. Research/chat module wired to OpenAI embeddings.
3. Simple React/Flutter client that demonstrates both features.
4. Setup scripts and README so I can spin everything up from scratch.
I plan on several prototyping cycles, so clear, modular code and incremental commits are key. If you have shipped similar AI features, send the repo links or a short demo video along with your proposed timeline for Sprint 1. I’m ready to start as soon as we agree on scope—looking forward to your ideas.
What must work from day one
• On-demand image rendering: when an agent uploads a photo (or none at all), the system should call the OpenAI API and DALL·E to generate or enhance listing visuals in real time, store the assets, and return URLs to the client app.
• AI-based research: buyers and agents will type or speak questions and receive chat-style answers that draw on listing data, local price trends, and comparable sales. I expect embeddings, retrieval, and summarisation to reside in a small Python or Node.js service for now.
Features to line up next (scaffolding now, polish later) include voice control via Whisper, invisible text layers for fuzzy voice search, duplicate-listing detection, an apartment-scanner that extracts room dimensions from user photos, price/sales comparison widgets, and an interactive map. Structuring the code so these modules can be toggled on or off is important.
Tech notes
• Front-end: React (web) or Flutter (mobile).
• Back-end: Node.js or Python with a clean REST/GraphQL layer.
• AI stack: OpenAI API is confirmed; Whisper and DALL·E are likely follow-ups.
• Dev environment: something Replit/Bolt-like for easy previews, yet the repo must stay in my GitHub with undisputed code ownership.
Deliverables – Sprint 1
1. Working image-rendering microservice (Dockerised).
2. Research/chat module wired to OpenAI embeddings.
3. Simple React/Flutter client that demonstrates both features.
4. Setup scripts and README so I can spin everything up from scratch.
I plan on several prototyping cycles, so clear, modular code and incremental commits are key. If you have shipped similar AI features, send the repo links or a short demo video along with your proposed timeline for Sprint 1. I’m ready to start as soon as we agree on scope—looking forward to your ideas.