AI-driven Inquiry-to-Conversion Platform boosts sales productivity by 2x for a leading Proptech firm
Our client is a Proptech firm that sells fractional ownership of luxury vacation homes with an 89% occupancy rate vs 39% for traditional second homes. We built an LLM-powered prospecting platform that reads large volumes of inquiries, scores them using signals, and generates tailored responses. The result was a measurable lift in qualification accuracy, faster sales cycles, and a more consistent pipeline flow.
Success Highlights
- 30% increase in conversion rate
- 2x improvement in sales productivity
- 50% growth in revenue through improved lead management
Key Details
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Industry: Proptech → Real Estate
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Geographies: United States
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Platform: Custom LLM-based Prospecting Engine LOS
Business Challenge
The client, a Proptech firm offering fractional ownership of luxury vacation homes, handled thousands of inquiries every month. Their team depended on spreadsheets and CRM exports to qualify prospects. This created delays, inconsistent scoring, and missed opportunities. Some of their major challenges were:
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Duplicate Inquiries: The team needed a dependable way to evaluate intent, budget fit, and readiness across inconsistent CSV files, emails, and form submissions. Also, manual review made it easy to miss high-potential customers.
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Prioritization Issues: Reps often spent hours sorting through records before reaching out. Without a shared prioritization model, sales efforts focused on whoever appeared active rather than the most qualified prospects.
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Inconsistent Data Formats: Inquiries arrived in different formats, and many were duplicates created over several months. The pipeline lacked a repeatable method for identifying quality leads.
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Data Analysis and Integration Issues: The team had historical data but no consistent way to structure it. Important signals in free text, including budget ranges, property types, and timelines, were never extracted or used for prioritization.
Our Solution Approach
We built a custom LLM-powered prospecting and lead-conversion platform that streamlined the entire process – from inquiry intake to personalized outreach.
Inquiry Analysis & Data Mapping
All incoming inquiries (CSVs, CRM logs, emails, website forms) were cleaned, merged, and transformed into rich buyer profiles. The platform extracted high-intent indicators such as: budget signals property preferences behavioral patterns engagement history This gave the client a single source of truth for every lead.
Unified Prospect Data Layer
We built a centralized knowledge layer that stores standardized attributes, extracted signals, embeddings, and historical interactions. This layer became the single source of truth for the scoring engine.It supports long-term learning because every conversion feeds back into the ranking logic.
LLM-Powered Prioritization & Outreach
We deployed a hybrid scoring model combining:structured ML predictions text embeddings for similarity matching. LLM-driven reasoning for contextual scoring. The system assigned a composite score to each inquiry based on fit, intent, and behavior patterns. High-priority prospects get immediate alerts.The platform also generates personalized email drafts, follow-up questions, and property recommendations tailored to each prospect’s profile.
Smart Insights & Continuous Learning
We created dashboards for sales teams showing lead rankings, inquiry patterns, and conversion predictors. The system relearned from new data and updates weights used in the scoring model. This ensured the ranking logic improves as more inquiries and conversions accumulate.
Technical Highlights
- Unified data from all sources
- AI extraction of buyer intent
- Smart LLM-powered lead scoring
- Instant high-intent prioritization
- Personalized AI outreach
- Continuous model improvement
- Scalable, high-volume architecture
- for inquiry in inquiries:
- features = extract_structured(inquiry)
- embedding = embed_text(inquiry.text)
- similarity = vector_db.search(embedding)ml_score = ml_model.predict(features)
- context_score = llm.score(inquiry.text)final_score = 0.40*ml_score + 0.35*similarity + 0.25*context_scoreif final_score > threshold:
- send("priority_follow_up", inquiry)
- else:
- send("nurture_message", inquiry)
Business Outcomes
The platform improved performance across the entire sales pipeline.
. Cleaner data and consistent prioritization ensured faster contact and more relevant conversations.
Reps no longer sifted through large spreadsheets. They focused on qualified prospects surfaced by the scoring engine.
. Better qualification expanded the pool of strong prospects and reduced leakage in the pipeline.