A new tool tackles a classic data engineering headache: deduplicating records with name variations, typos, and career changes. Traditional fuzzy matching struggles with semantic duplicates like 'A. Butoi' vs 'Alexandra Butoi'. ChatGPT over-merges, losing 88 clusters. The new everyrow.io/dedupe service uses LLMs for contextual judgment, achieving 98% row accuracy on a 200-profile test set.

The system accepts a natural-language equivalence definition, like 'same person despite email changes or name formats'. It clusters items by embedding similarity first, then performs pairwise LLM comparisons to build an association graph. While processing takes 90 seconds and costs $0.42 for 200 records, it avoids the threshold trade-offs that plague fuzzy matching and the false merges common with naive LLM prompts.

For developers, the tool is available via a Python SDK. The key trade-off is speed versus precision: fuzzy matching is 2000x faster but has a 12-16% accuracy gap. This approach is best when semantic judgment is critical and no single reliable key exists, such as merging records for academic researchers or customer profiles with inconsistent data entry.