The Challenge


Procogia’s Approach

We developed natural language processing (NLP) and machine learning (ML) solutions that presented customers with better image search results.


  • We used a client-specific corpus of historical search queries as the basis for a custom spell check dictionary, ensuring our product could effectively handle the types of language search customers were using
  • We wrote a spell checker in Python implementing NLP concepts and techniques, including Damerau-Levenshtein distance, phonetic encoding, grammatical knowledge, and probabilistic typo identification
  • We used both manual evaluation and parameter tuning to iteratively optimize the model
  • To facilitate internationalization beyond English, we ensured our algorithms could be applied to any alphabetic language.

The Results


  • A/B testing found a 39% higher purchase rate for searches corrected by the spell checker vs. the control group
  • Our product was 50x less likely to give false corrections than the out-of-the-box model the client was using
  • ProCogia’s spell checker now handles all searches on the client’s US site and is currently being extended to non-English languages including French and German
  • The spell check solution has resulted in customers successfully finding their desired images and therefore increased revenue for the client.


Services Used

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