A large-scale real estate platform operating across India connects property buyers, renters, and owners directly - without broker intermediaries. With 180,000+ leads generated monthly across web and app, the business had built significant top-of-funnel scale. The conversion infrastructure hadn't kept pace.
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call coverage - up from 60% manual sampling
improvement in agent utilisation
calls scored monthly across 12 QA parameters
High lead volume was masking a serious funnel problem. Only 8–10% of leads converted to site visits. Between 35–40% were never contacted within the first 24 hours - buyers who had expressed intent and then heard nothing. 60% of sales calls had zero quality review. There was no scoring, no routing logic, no intent classification, and no way to know which agents were performing or why.
Industry: Real Estate / PropTech
Services: AI Conversion Intelligence, Lead Scoring, Conversational AI, QA Automation
Region: India / South Asia
A four-layer intelligence stack built to process 25,000+ monthly conversations across voice and chat.
Data Layer Web behaviour, CRM records, and call recordings unified into a single foundation - every touchpoint structured for model training and real-time scoring.
Intelligence Layer - Lead Scoring A scoring model trained on 300K+ historical interactions assigns each lead a score from 0 to 100. Leads above 70 are routed immediately to top agents. Medium-intent leads are handled via assisted automation. Low-intent nurtured and re-scored over time. A continuous score was chosen over fixed tiers so the model self-adjusts as market conditions shift - no manual recalibration needed. See how we build AI-led scoring systems.
Execution Layer - Conversational AI AI-assisted call handling across inbound and outbound flows, with multilingual support across six Indian languages. Budget, location, urgency, and buying intent are extracted automatically from every conversation. Routing rules kept up-to-date without engineering involvement using a low-code rules engine - first response time for high-intent leads cut from 45 minutes to under 5 minutes.
Feedback Layer - QA and Agent Intelligence: Every call scored across 12 parameters - no sampling, full coverage. Violations flagged in real time. Top-performing conversation patterns clustered into coaching signals per agent. The model surfaced a recurring registration cost objection buried in lost deals that manual review had never caught - fed directly into training. Tracked through a BI reporting layer built for non-technical users.
Before the system, the compliance team spent most of their time reviewing a 60% sample and still missed things. After deployment, they moved entirely into remediation and coaching - because the model was catching everything upstream. The head of sales operations noted that the biggest shift wasn't the conversion numbers - it was that managers stopped having opinions about which agents were underperforming and started having evidence.
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