India Location Normalizer

Resolve messy India locality aliases into canonical location fields without side effects.

Quick Triggers

• Normalize Mumbai/Pune location aliases from extracted leads.
• Map PCMC and Hinjewadi variants to canonical localities.
• Resolve Mumbai shorthand like Scruz, Khar, Andheri W, Turner Road, Carter Road.
• Standardize locality names before scoring or storage.

Recommended Chain

message-parser -> lead-extractor -> india-location-normalizer -> sentiment-priority-scorer

Target KPI for production tuning: improve canonical Mumbai/Pune locality resolution versus extractor-only baseline.

Execute Workflow

1. Accept lead-location payload from Supervisor.
2. Validate input against references/location-normalizer-input.schema.json.
3. Use references/india-location-aliases-v1.json as the authoritative lookup map.
4. Match in this order:
   • exact alias match (case-insensitive)
   • token-normalized alias match (trim punctuation, collapse spaces)
   • conservative fuzzy match only when clearly unambiguous
5. Return one normalized location record per input lead with:
   • city
   • locality_canonical
   • micro_market
   • matched_alias
   • confidence
   • unresolved_flag
6. Validate output against references/location-normalizer-output.schema.json.

Enforce Boundaries

• Never parse raw chat exports.
• Never extract non-location entities.
• Never write to Google Sheets, databases, or files.
• Never send messages or trigger external channels.
• Never auto-resolve low-confidence ambiguous aliases.

Handle Ambiguity

1. If multiple localities match equally, set unresolved_flag: true.
2. If no confident match exists, preserve input in matched_alias and mark unresolved.
3. Prefer false-negative over false-positive for city/locality assignment.