San Francisco, CA
A San Francisco real estate agency was spending enormous agent time on leads who were not ready to buy — showings for mismatched properties, extended search timelines, and a CRM that was essentially a spreadsheet with no intelligence. Top agents were spending 60% of their time on low-probability leads while high-intent buyers were being handled with the same approach as early-stage prospects.
We built an AI property matching engine that scored inbound leads on 24 behavioral signals — search patterns, property views, filter usage, engagement timing — and automatically prioritized the highest-intent buyers for immediate agent follow-up. Virtual tour integration reduced unnecessary showings. An AI-driven neighborhood analytics dashboard gave buyers data-rich insights that accelerated decision confidence.
Analyzed 18 months of lead data to map the behavioral signals that predicted high-intent buyers versus early-stage researchers.
Built the property matching AI and lead scoring model, integrating with MLS data and the agency's existing CRM.
Integrated 3D virtual tours for all listings, enabling buyers to eliminate mismatched properties before scheduling physical showings.
Built the neighborhood analytics dashboard providing buyers with price trend data, school ratings, and commute analysis that reduced decision timeline.
AI lead scoring increased the percentage of leads converting to signed agreements from 8% to 15%.
Average time from first contact to signed purchase agreement dropped from 94 days to 31 days.
Improved close rate and client satisfaction generated referral momentum that grew active listing count 62%.
Virtual tours eliminated 45% of low-probability showings, freeing significant agent time for high-intent buyers.
“My agents now spend their time on people who are actually ready to buy. The AI tells us who to call first and why. Our close rate has nearly doubled.”
Let’s discuss how Calidigi can deliver results like these for your industry — with a custom strategy built around your goals.