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Über
LISTR is a humanistic conversational AI that acts like a personal leasing concierge for people searching for a place to live. It speaks naturally, asks the right questions at the right time, and guides renters through the entire leasing process—helping them understand their options, narrow down what fits their lifestyle and budget, and move confidently from browsing to touring to applying. Instead of forcing people into forms or leaving them waiting on slow responses, LISTR feels like texting a knowledgeable, organized human who actually listens. Behind the scenes, it connects renters with realtors and brokers, builds curated property lists, coordinates tours, and manages follow-ups—removing confusion for renters and workload for agents, while keeping the experience personal, responsive, and stress-free.
What the App does:
LISTR is an AI leasing operating system built for realtors, apartment locators, and brokerages. It automates the entire leasing workflow—from social DMs, texts, calls, and emails to tours, applications, follow-ups, and payouts—so agents stop juggling tools and stop losing leases. LISTR handles lead intake, renter qualification, listing curation, tour coordination, document collection, application follow-up, and commission tracking in one system, while letting agents step in at any time. The result is faster responses, fewer no-shows, higher close rates, and more leases closed with less manual work.
Website:
Steps of how it works:
For Renters (Looking for a Place)
-Texts or DMs a realtor.
-LISTR replies instantly like a human.
-Asks simple questions (budget, location, pets, timeline).
-Builds a live preference profile.
-Finds matching apartments or rentals.
-Sends a clean, swipeable list.
-Renter thumbs up/down options.
-Prequalifies renters based on their income and credit.
- Schedules tours automatically.
-Collects docs and submits applications.
-Follows up until approved and moved in.
For Realtors / Apartment Locators
-Connect socials and phone number.
-LISTR answers all inbound DMs & texts.
-Qualifies renters automatically.
-Builds and updates property lists.
-Handles follow-ups and re-engagement.
-Schedules tours intelligently.
-Collects documents and applications.
-Tracks approvals and payouts.
-Flags issues and no-shows.
-Realtor steps in anytime to close.
For Brokers
-Invites agents into LISTR.
-Sees all agent activity in one dashboard.
-Tracks leads, tours, and closings.
-Monitors response times and performance.
-Identifies top and underperforming agents.
-Standardizes leasing workflows.
-Reduces missed leads and churn.
-Scales leasing output across the team.
-In One Line
-Renters get guided, stress-free leasing.
-Realtors close more with less admin.
-Brokers gain visibility, leverage, and scale.
Core components / Tech Stack:
Lovable (Admin Panel)
AWS
Github
Mongo DB
Postman
Langchain
Mandatory deliverables & Non negotiables:
A fully functional admin dashboard (admin flows → UI → API → DB → runtime)
Property List logic and algorithm
Clear and concise communication
Availability:
Weekends and weekdays
Video Library
App walkthrough:
Dash walkthrough:
API / LLM Walkthrough: Conversation Brain Documentation = ATTACHED
Dev review of what needs to be done:
After reviewing this documentation.. You should be able to:
-Walk me step-by-step through what happens when a renter sends a message to LISTR — from webhook trigger to final response — including how state is managed, how prompts are constructed, and how database writes occur.
-After reviewing the documentation, explain what structurally makes our conversational AI humanistic rather than robotic.
-Break down the conversational brain architecture. What layers exist in the prompting system, what does each layer do, and why is it layered that way?
-If a change is made in the admin dashboard (persona, response rules, objectives, filters), explain exactly how that change propagates to the live bot in production.
-How does the system maintain conversation continuity across sessions and prevent re-asking questions it already knows?
-If MongoDB successfully writes data but the LLM response fails or times out, what state is preserved and how would you prevent inconsistency or corruption?
-If you had to rebuild this entire system from scratch in 30 days, what architecture would you choose and why?
When stepping into a complex codebase built by previous developers, what is your process for understanding the system deeply without breaking production?
-How would you structurally enforce tone control so the bot never sounds technical, interrogative, or robotic, even if prompts are modified in the admin panel?
-After reviewing all documentation, what do you believe is the most technically complex or fragile part of this system, and why?
You must be experienced in working with software and app products in real estate, leasing, and Conversational AI.
Please provide references of projects that we can verify and also send a detailed video explaining your understanding of what we need done and how you will implement this and your rough estimated time line.
ANY REPLIES WITH OUT A RESPONSE VIDEO WILL BE IGNORED.
We are looking to hire some one TODAY
Contract duration of 1 to 3 months. with 30 hours per week.
Mandatory skills: Python, , OpenAI API, GPT-4 API, Whisper API, GPT API, ChatGPT API, AI Model Integration, API Integration, Application Integration, Chatbot Integration, API Testing, Integration Testing, Website Integration
Sprachkenntnisse
- English
Hinweis für Nutzer
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