Cut the cost of cleaning
global place names.
Automated name cleaning with a production model that reduced inference cost by 98%.
Place records contain names in many languages, often mixed with promotional text and other clutter. The existing generative approach cost $70,000 per run and could introduce words that were never in the source name.
I trained a model to decide which parts of the original name to keep or remove. This made the task more focused and constrained the output to text already in the record.
The business result: substantially lower operating cost for a recurring data-quality task, with a model designed around the actual cleanup problem.
Read about the model and design decisions →
