AI API for real estate in India
Property listings, lead scoring, and valuation insights powered by unoblox's ₹-native AI API — one key, every model, billed monthly in rupees.
Real estate in India is data-rich but under-leveraged. Property listings vary wildly in quality; leads are unqualified; agents spend hours on manual tasks. AI can write compelling property descriptions, score buyer intent, and estimate market value—but only with data you can audit. unoblox connects your real estate stack to world-class reasoning models, billed in rupees.
Property description generation
Qwen3.8-27B (₹16.32 / ₹48.96) generates SEO-optimized property descriptions from your listing basics (BHK, location, amenities, price). Feed it a photo + floor plan; it writes natural, persuasive copy that drives clicks and inquiries. Regenerate for different buyer personas (families, investors, first-time buyers).
Lead scoring & intent detection
Qwen3 Max (₹120.95 / ₹604.77) analyzes buyer interactions (listing views, call logs, site visits, property comparisons) to predict purchase intent and timeline. High-intent leads get prioritized; tire-kickers are nurtured. Combine with embeddings (₹0 freemium) to cluster buyers by investment profile.
Market valuation & insights
GPT-4.1 (₹201.6 / ₹806.4) analyzes comparable properties, neighborhood trends, and price history to estimate market value. An agent submits a new listing; GPT-4.1 suggests a competitive price range + confidence. Reduce negotiation time and underpricing.
| Use-case | Recommended model | ₹ input/output | Why |
|---|---|---|---|
| Property copy | Qwen3.8-27B | ₹16.32 / ₹48.96 | SEO-optimized, persuasive |
| Lead scoring | Qwen3 Max | ₹120.95 / ₹604.77 | Intent prediction |
| Market valuation | GPT-4.1 | ₹201.6 / ₹806.4 | Comparative analysis |
| Buyer clustering | Embeddings | ₹0 (freemium) | Segmentation by profile |
Frequently asked questions
Can AI estimate property value accurately? AI predicts fair market range; it's a starting point, not gospel. Use for competitive pricing and faster negotiations, not loan appraisals (which require certified valuers).
How do I handle regional real estate jargon (sq. ft. vs. sq. m., carpet area vs. built-up)? Train the model on examples of your market's terminology. Include a style guide in the system prompt; AI learns regional norms quickly.
Can AI write descriptions that attract investors? Yes. Provide investor profiles (ROI threshold, hold period, tenant profile); Qwen3 Max tailors the narrative to highlight cashflow potential and appreciation.
What if my property photos are low quality? AI uses text fields primarily (location, amenities, price, size). Poor photos don't block AI copy generation; just limit the photo-to-description use-case. Encourage agents to take better photos over time.
Can I use embeddings to find similar past sales? Yes. Embed anonymized sold properties; when a new listing arrives, search for comparables. This accelerates valuation and competitive positioning.
Is there a free tier to test property listing AI? Yes. Qwen3-1.7B (₹0) generates basic descriptions. Upgrade to Qwen3.8-27B or Max for higher quality and regional language support.
Get started in rupees → https://unoblox.ai/sign-in
Start building in rupees
Call every major model through one OpenAI-compatible endpoint, billed in ₹ on a GST invoice.