AI Leasing Chatbot for Property Lead Qualification
Transforming Messenger inquiries into guided property searches, qualification, and actionable lead records.
A well known property leasing corporation in the Philippines that handles residential units and event venues needed a more consistent way to manage Facebook Page and Messenger inquiries. SOFI AI was tasked to design an AI leasing chatbot that could support lead generation, answer common questions, and guide prospects toward an appropriate next step. The challenge extended beyond FAQs; inquiries arrived through page messages, advertisements, and public comments. While relevant information are sorted for properties, venues, forms, ads, feedback, and unanswered questions. Without a structured flow, each conversation could become a separate manual search and qualification task. SOFI AI translated those requirements into a branching customer journey. The assistant identifies the inquiry type, then gathers practical preferences such as location, finish, bedrooms, budget, lease term, or venue capacity. It can then present relevant information, send images or forms, and collect contact details for follow-up. The project created a foundation for organized inquiry handling without removing human accountability. Undefined questions, negative feedback, escalation rules, fallback responses, and restricted topics were treated as operational requirements. Several controls remained subject to client confirmation before full rollout.
The Business Challenge
The organization’s conversations covered general leasing questions, available units, rent inquiries, venue requirements, advertising responses, and comment-to-inbox engagements. These requests entered through the same social channels but required different information and follow-up paths. Staff had to interpret each message, identify the relevant property or venue, ask qualifying questions, locate supporting materials, and decide whether to send a listing, image set, script, or form. Repeating those steps could create uneven responses and make lead information harder to organize. Customer needs also varied by city, property, unit type, budget, lease duration, and expected venue capacity. Language and tone preferences including whether to use “Po” and “Opo” also shaped the experience. Thus, generic FAQ bot would not have been sufficient. SOFI AI needed to combine conversational guidance, preference capture, media delivery, lead recording, and clear routes for uncertain or sensitive cases.
The Solution & User Experience
Discovery and Prioritization
The engagement began with onboarding and workflow definition, where SOFI AI aligned business goals, conversation flows, response guidelines, and escalation rules to support accurate intent classification, customer engagement, and case handling.
A Knowledge Structure Built Around Existing Operations
The assistant was designed around the organization’s working sheets, using FAQs, property details, forms, feedback logs, and operational data to provide consistent responses and support staff follow-through.
Customer Experience
The AI leasing chatbot routes conversations by inquiry type, asking only relevant questions before providing FAQs, recommending units or venues, sharing media or forms, or collecting customer contact details for follow-up.
Workflow Automation and Lead Capture
The workflow guides inquiries through structured steps, captures relevant customer information, supports configurable media delivery, and records qualified leads for organized follow-up and endorsement.
Responsible AI by Design
The project emphasized human oversight by tracking unresolved inquiries, defining escalation and fallback rules, and using a phased rollout to refine the assistant based on real conversations.
Business Impact
The designed solution established a structured operating model for digital leasing inquiries by guiding customers through preference-based conversations, capturing actionable lead information, ensuring consistent inquiry handling, and improving Messenger engagement. It also supported continuous improvement through tracking knowledge gaps, monitoring feedback, and refining conversation flows, media delivery, and escalation practices over time.
Frequently Asked Questions
It asks preference-based questions before presenting options or endorsing a lead. The designed flow considered location, finish, bedrooms, budget, lease duration, property or unit type, and venue capacity, then collected contact details when appropriate.
The design used working sheets for FAQs, property directories, venues, forms, operational details, advertisements, unanswered questions, and feedback. Any additional database or system connection would require separate verification and configuration.
Yes, the planned scope included advertisement IDs, promotion handling, comment replies or reactions, and movement from public comments into private Messenger conversations. Exact campaign scripts, featured-item details, and handling rules still required final client alignment.
It can be refined where approved data and business rules are available. Potential extensions include additional property or venue branches, more detailed follow-up sequences, refined ad scripts, and media formats tailored to different inquiry types.