Fuzzy String Matching + AI Verification with Fenic — Catch Duplicates (in 120 Seconds)
Find duplicates despite typos, then verify them with AI.
In this 2-minute demo, we use Fenic to combine fuzzy string matching algorithms with AI reasoning for near-perfect duplicate detection. Traditional deduplication either misses real matches or merges records that don’t belong together. Fenic handles both — automatically.
First, we use built-in fuzzy matching methods like Levenshtein distance and token sort ratio to find potential matches. Then, Fenic’s semantic.map() uses AI to verify uncertain cases using context like email domains and company names.
🔑 What you’ll see:
🔍 Multiple fuzzy matching algorithms (Levenshtein, token-based)
🧠 AI verification for borderline cases
⚖️ Hybrid precision — algorithms + contextual reasoning
✅ Clean, verified customer data
This hybrid approach delivers the best of both worlds: speed from algorithms, precision from AI. No over-merged records, no missed duplicates — just clean, trustworthy data without manual review.
👉 Try the Colab demo: https://colab.research.google.com/git...
👉 GitHub: https://github.com/typedef-ai/fenic/t...
👉 Docs: https://docs.fenic.ai/latest
👉 Join the community: / discord
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