Teenva AI & Digital Ventures builds property recommendation AI for property portals, broker networks, builders, and proptech startups from Bangalore, India. Buyers and renters describe needs in plain language—3 BHK under ₹1.2 Cr near Whitefield with gym—and receive ranked listings from your inventory with explainable match reasons, while agents get lead–property fit scores in Zoho CRM or your custom portal.
Personalized rank, natural-language search, and agent lead scores—on your catalog only. Static filters alone miss intent; Teenva combines behavioral ML, listing embeddings, geo rules, and business constraints.
Build your AI solution with us
Property recommendation AI—ML matching, NL search, and CRM lead scores on your listings.
Recommendation surfaces we power
| Surface | User | Goal |
|---|---|---|
| Homepage | Returning visitor | Continue your search picks |
| Search results | Active browser | Re-rank by learned preferences |
| Listing detail | Property viewer | Similar homes / Same society |
| Lead form follow-up | New inquiry | Email/WhatsApp digest of top 5 |
| Agent CRM | Sales rep | Match score vs new inventory |
| Project microsite | Builder buyer | Tower/floor/plan recommendations |
| Rent vs buy router (scoped) | Undecided user | Segment-appropriate inventory |
Each surface has its own model strategy, diversity rules, and A/B hook—not one widget copy-pasted everywhere.
Feeds better picks into listing content AI. Parallel craft to product recommendation engine. Hub: AI solutions.
Signals & ranking approaches
| Signal | Use |
|---|---|
| Explicit filters | Budget, BHK, buy/rent, locality, possession |
| Implicit behavior | Views, saves, contact clicks, time on map |
| Listing attributes | Carpet/built-up, floor, facing, amenities, age |
| Geo | Distance to landmark, commute time band (scoped) |
| Collaborative | Leads with similar behavior liked these units |
| Content embeddings | Description + image tags for cold-start projects |
| Business rules | Only available units; prioritize mandated inventory |
| LLM (assistive) | Parse NL query → structured filters; why matched blurb |
Core rank order comes from ML + rules on your listings API—not hallucinated addresses.
Features we implement
Recommendation API
GET /recommend?user_id=&slot=search&listing_id=Natural-language search
2 bed flat Indiranagar terrace → query objectLead match score
0–100 fit for agent callback priorityMap-aware rank (scoped)
Boost inside drawn polygon or commute isochroneNew project cold start
Launch inventory via attributes before traffic existsAgent overrides (scoped)
Boost featured builder campaigns with auditEvent pipeline
Views, leads, site visits from web + mobile appCRM sync
Recommended list attached to lead in Zoho CRMWhatsApp digest (scoped)
Top picks via WhatsApp APIAnalytics
CTR, lead-to-visit, recommendation-attributed deals (scoped)
CRM sync via Zoho CRM. Events from mobile app. WhatsApp via WhatsApp API.
Architecture
Portal / app events + listing catalog API
Feature store (user, session, listing vectors)
Ranker (collaborative + content + rules)
Explain layer (top 3 match reasons)
UI slots + CRM webhook + optional WhatsApp
Outcome feedback (visit booked, deal stage) → retrain
Latency — < 100–300 ms per slot at scale (scoped targets)
Freshness — sold/rented units removed from rank in near real time
Use cases by business model
| Model | Recommendation focus |
|---|---|
| City portal | NL search + personalized home feed |
| Broker CRM | Score new leads against active mandates |
| Builder sales | Project/phase fit; inventory clearance |
| Rental marketplace | Budget + commute + furnishing match |
| NRIs (scoped) | Locality education + shortlisted digest |
| Commercial (scoped) | Seat count, parking, lease type filters |
India-specific considerations
| Topic | How we handle |
|---|---|
| Carpet vs super built-up | Normalize in catalog schema; show both in UI |
| Locality aliases | Koramangala 5th block ↔ geo polygon |
| Budget in ₹ | Lac/Cr parsing in NL queries |
| RERA / possession | Filter and badge from listing fields—not legal advice |
| Vastu / facing (scoped) | Optional preference weight if you capture it |
| Languages (scoped) | English + Hindi query parse with evals |
Recommendations only work on clean inventory data. We align listing schema, availability, and geo before tuning the ranker.
Property recommendation vs listing content AI
| Product | Focus |
|---|---|
| Property recommendation AI (this page) | Who sees which listing—rank & match |
| Listing content AI | Write titles, descriptions, SEO copy |
| Product recommendation engine | Retail SKUs—same ML patterns, different domain |
| Visual product search | Image-similar products—not property rank (unless scoped add-on) |
Why Teenva AI
Recommendation + portal build
Web + ML in one teamProven ecommerce ML
Adapted to high-consideration real estate journeysCRM-native
Zoho and custom stacksBangalore proptech hub
Local market semantics in discoveryContent synergy
Ranker + listing content for launch velocity
Portal build via web development. CRM via Zoho. Content synergy with listing content.
Our delivery process
Discovery
Inventory source, channels, CRM, KPI (leads, visits)
Catalog audit
Fields, geo, availability sync quality
Event instrumentation
Pixel/server events on portal
Baseline ranker
Rules + popularity vs ML holdout
NL search pilot
Parse queries; measure zero-result rate
Slot rollout
Search, PDP similar, lead email
CRM integration
Match score on lead record
A/B test
Recommendation on vs off
Retrain cadence
Weekly/monthly with new deals feedback (scoped)
Frequently asked questions
No—rank only returns IDs from your catalog API.
Rough guide: 1k+ listing views/month for collaborative signals; cold start uses attributes earlier.
Yes—recommend from inventory feed to score new leads for agents (scoped).
- Yes—match lists and scores on lead/deal records via Zoho integration.
Scoped with eval set—not translate-only.
- Rank vs write—complementary products. See listing content AI.
Geo polygon and isochrone boost (scoped) when data available.
No—display fields from listing; legal questions to human agents.
- Yes—API for cross-platform app.
10–14 weeks MVP—events, rank API, search re-rank, one CRM hook.
Build your property recommendation engine
Personalized listing rank, natural-language search, and lead matching for portals, brokers, and builders.
Build your AI solution with us
Ready to take your business to the next level?