HomeSuccess StoriesReal EstateAI-driven Prospecting Platform boosts sales productivity by 2x for a leading Proptech firm

AI-driven Prospecting Platform boosts sales productivity by 2x for a leading Proptech firm

Success Highlights

30% increase in conversion rate
2x improvement in sales productivity
50% growth in revenue through improved lead management

Key Details

Industry: Proptech → Real Estate Geographies: United States

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:

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.
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.
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.
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.

1 · Discover

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.

 

2 · Consolidate

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.

3 · Automate

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.

4 · Deploy

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_score

if final_score > threshold:
send(“priority_follow_up”, inquiry)
else:
send(“nurture_message”, inquiry)

 

Business Outcomes

The platform improved performance across the entire sales pipeline.

30%

Boost in conversion rate. Cleaner data and consistent prioritization ensured faster contact and more relevant conversations.

2x

Increase in sales productivity Reps no longer sifted through large spreadsheets. They focused on qualified prospects surfaced by the scoring engine.

50%

Growth in revenue through improved lead management. Better qualification expanded the pool of strong prospects and reduced leakage in the pipeline.

  Streamlined Data Flow: Simplified visibility and tracking of all inquiries.   Faster Conversions: Intelligent prioritization ensured timely follow-up.   Improved Buyer Experience: Personalized, timely responses built trust and improved customer satisfaction.

Ready to Transform Your Prospecting Process?

Talk to our engineers about implementing LLM-powered automation and AI-based lead prioritization to modernize your Proptech operations.

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