AI Systems
AI Data Mapper
Python and Gemini read legacy CRM headers in plain language and map them automatically — three days of work down to under an hour.
- Python
- Google Gemini 1.5
- NLP
Results
- Migration time
- 3 days → <1 hr
- Junk data imported
- Zero
- Manual field mapping
- Eliminated
- Reusable across clients
- Yes
The brief
Every CRM migration stalls on the same problem: nobody can tell what the old system's column headers mean. I built a mapper that reads them with an LLM, matches them to the target fields, and flags junk and duplicates before they contaminate the new database.
How it was built
- 01
Read the headers
Gemini interprets legacy column names in natural language, including the abbreviations and typos that defeat rule-based matching.
- 02
Propose the mapping
Each source field is matched to its target with a confidence score, so a human reviews the uncertain ones instead of all of them.
- 03
Flag before import
Junk rows and duplicates are caught pre-import — the cheapest possible moment to catch them.
- 04
Import clean
Only validated, mapped records reach the CRM, which is what made the five-tool consolidation a zero-data-loss migration.
Built with
- Python
- Google Gemini 1.5 Flash
- GoHighLevel API