| **PROJECT OVERVIEW** |
|
|
|
|
|
|
| **Project Name:** AI-Powered Property Recommendation via Contact Request \& CRM\*\* |
|
|
|
|
|
|
| **Overview** |
|
|
|
|
|
|
| This project builds an AI-driven recommendation system that responds to user property requests submitted through the \*\*Contact Us\*\* form on a real estate website. |
|
|
|
|
|
|
| When a user submits a request specifying budget, location, property type, and preferences, the request is: |
|
|
|
|
|
|
| 1\. Stored in the **CRM** as a lead |
|
|
| 2\. Processed by the backend |
|
|
| 3\. Passed into a vector search engine |
|
|
| 4\. Used to retrieve the most relevant property listings |
|
|
| 5\. Returned to the user via the **website chat interface** as recommended property links |
|
|
|
|
|
|
| This system improves lead response speed, relevance, and conversion by providing instant, intelligent property suggestions |
|
|
|
|
|
|
| **MVP(Minimal Viable Product) DEFINITION** |
|
|
|
|
|
|
| **MVP Goal** |
|
|
|
|
|
|
| Deliver **instant property recommendations** based on user specifications from the Contact Us form, while logging the request in the CRM |
|
|
|
|
|
|
| **MVP Scope (Phase 1)** |
|
|
|
|
|
|
| User Flow |
|
|
|
|
|
|
| 1\. User clicks **Contact Us** |
|
|
| 2\. User submits: |
|
|
|
|
|
|
| \* property type |
|
|
| \* location preference |
|
|
| \* budget |
|
|
| \* specifications |
|
|
|
|
|
|
| 3\. Request is: |
|
|
| saved in CRM |
|
|
| sent to recommendation engine |
|
|
|
|
|
|
| 4\. User receives: |
|
|
| 5–10 recommended property links in chat |
|
|
|
|
|
|
| **Components** |
|
|
| * Contact Us form |
| * Backend API to receive lead |
| * CRM lead ingestion |
| * Vector search–based recommendation engine |
| * Chat response with property cards or links |
|
|
|
|
|
|
| **Recommendation Logic (MVP)** |
|
|
| * **Hard filters:** budget, listing type, location |
| * **Vector similarity:** semantic matching of property descriptions |
| * **Re-ranking:** closeness to budget + preference match |
|
|
|
|
|
|
|
|
|
|
| **Out of Scope (For MVP)** |
|
|
| * Personalized long-term user profiles |
| * Payment or booking |
| * Mobile app |
| * Analytics dashboard |
| * Agent assignment logic |
|
|
|
|
|
|
|
|
|
|
| **MVP Success Criteria** |
|
|
| * Recommendation response time < 2 seconds |
| * At least 70% relevance in manual review |
| * Works with ≥ 1,000 property listings |
| * CRM successfully stores every request |
|
|
|
|
|
|
|
|
|
|
| **ROLE ASSIGNMENT (TEAM OF 4–5)** |
|
|
|
|
|
|
| **AI / ML Lead** |
|
|
| Responsibilities: |
|
|
| * Define recommendation strategy |
| * Design embedding \& vector search pipeline |
| * Build Colab notebooks |
| * Define input/output schemas |
| * Validate recommendation quality |
|
|
|
|
|
|
| Ownership: |
|
|
|
|
|
|
| * Vector index |
| * Query logic |
| * Recommendation relevance |
|
|
|
|
|
|
| **Backend Engineer** |
|
|
| Responsibilities: |
|
|
| * Contact Us API endpoint |
| * CRM ingestion |
| * Recommendation service orchestration |
| * Chat response handling |
|
|
|
|
|
|
| Ownership: |
|
|
|
|
|
|
| * API contracts |
| * Service reliability |
|
|
|
|
|
|
| **Frontend Engineer** |
|
|
| Responsibilities: |
|
|
| * Contact Us form UI |
| * Chat interface UI |
| * Rendering recommendation cards/links |
|
|
|
|
|
|
| Ownership: |
|
|
|
|
|
|
| * User experience |
| * API integration |
|
|
|
|
|
|
|
|
|
|
| **Data / Infra Engineer (Optional but valuable)** |
|
|
|
|
|
|
| Responsibilities: |
|
|
|
|
|
|
| * Property dataset preparation |
| * Data cleaning \& normalization |
| * Storage \& deployment setup |
|
|
|
|
|
|
| Ownership: |
|
|
|
|
|
|
| * Data quality |
| * Environment stability |
|
|
|
|
|
|
|
|
|
|
| **Leadership rule:** |
|
|
| You own decision clarity, not all the code. |
|
|
|
|
|
|
|
|
|
|
| **DATASET PREPARATION (YOUR FIRST TECH TASK)** |
|
|
|
|
|
|
| **Required Property Dataset Fields** |
|
|
| Each property listing **must** include: |
|
|
|
|
|
|
| | Field | Description | |
|
|
| | ------------- | ------------------------------ | |
|
|
| | property\_id | Unique identifier | |
| |
| | title | Short property title | |
| |
| | description | Full description | |
| |
| | listing\_type | sale / rent / shortlet | |
|
|
| | property\_type | apartment / duplex / land | |
| |
| | price | Numeric value | |
| |
| | location | City / area | |
| |
| | bedrooms | Integer | |
| |
| | bathrooms | Integer | |
| |
| | amenities | List (parking, security, etc.) | |
| |
| | url | Public listing link | |
| |
| | status | available / unavailable | |
| |
| |
| |
| |
| |
| **Fields Used for Vector Embeddings** |
| |
| |
| |
| These fields are **combined into one semantic text**: |
| |
| |
| |
| * title |
| * description |
| * amenities |
| * location |
| * property\_type |
| * listing\_type |
| |
| |
| |
| Example combined text: |
| |
| |
| |
| > “3 bedroom furnished apartment in Lekki Phase 1 with parking, 24-hour security, close to the beach.” |
| |
| |
| |
| |
| |
| Fields Used for Filtering (NOT embeddings) |
| |
| |
| |
| * price |
| * listing\_type |
| * location |
| * bedrooms (if strict) |
|
|
|
|
|
|
| **Dataset Sources** |
|
|
| * Existing company database |
| * Export from CMS |
| * Scraped listings (if approved) |
| * Mock dataset (for early MVP) |
|
|
|
|
|
|
| **Output of Dataset Preparation** |
|
|
| * Cleaned property table |
| * Text field for embeddings |
| * Metadata table for filters |
| * Ready for FAISS indexing |
|
|
|
|
|
|
|
|
|
|
| **CRM INTEGRATION (MVP VIEW)** |
|
|
| CRM Receives: |
|
|
| * User contact info |
| * Property request specs |
| * Timestamp |
| * Recommendation IDs returned |
|
|
|
|
|
|
| Why this matters: |
|
|
| * Agents can see what was recommended |
| * Follow-up becomes context-aware |
| * Better conversion tracking |
|
|
|
|
|
|
|
|
