Lead Extractor

Identify lead signals in parsed messages and emit strict lead objects.

Quick Triggers

• Find all buyer leads from this WhatsApp chat.
• Extract contact details and budget from these messages.
• Identify serious property inquiries from parsed messages.

Recommended Chain

message-parser -> lead-extractor -> india-location-normalizer

Execute Workflow

1. Accept parsed messages from Supervisor.
2. Validate input with references/parsed-message-input.schema.json.
3. Apply chat-specific extraction rules from references/extraction-rules-re-india-v1.md.
4. Determine dataset_mode from Supervisor context:
   • default: broker_group
   • allowed: broker_group, buyer_inquiry, mixed
5. Detect lead-candidate messages using inquiry intent, contact details, and property-related preferences.
6. Classify record_type:
   • inventory_listing for broker inventory/availability posts (default in broker groups)
   • buyer_requirement for explicit "required/chahiye looking for" demand posts
   • drop non-lead/system noise instead of emitting noise_or_system
7. Handle multiline listings as one candidate record when body lines contain price, area, or location details.
8. Build lead records with:
   • required: lead_id, name, phone, record_type
   • optional: dataset_mode, property_type, budget, deal_type, asset_class, price_basis, area_sqft, area_basis, location_hint, raw_text, source, created_at
9. Normalize phone extraction from spaced variants such as +91 98205 82462 and 98200 78845.
10. Distinguish price intent from rate intent:

• examples: 3.5 Lakh rent (monthly), 60K psf (per-sqft), 4.25 Cr (total)

11. Deduplicate leads by stable keys when records clearly refer to the same person.
12. Validate output with references/output-leads.schema.json.
13. Return only validated lead objects.

Enforce Boundaries

• Never write or update persistent storage.
• Never modify source messages.
• Never generate summaries.
• Never suggest or execute follow-up actions.
• Never send communication or invoke external side effects.

Handle Errors

1. Reject invalid parsed-message input.
2. Emit an empty array when no lead evidence exists.
3. Return field-level validation errors when extracted records violate schema.