AI-driven search: understands customer intent
Software Development, Data Science
A leading electronic component distributor approached us to automate their online purchasing workflow.
Challenges
Their customers typically submit lists with product descriptions in free-form text, which must be manually matched to actual products by humans.
Solution
We've developed an advanced NLP model that leverages Named Entity Recognition (NER) to extract key product parameters—such as electrical units, part numbers, dimensions, and case codes—from customer search queries. This enables precise and efficient product searches by automatically matching user intent with relevant products.
Outcome
Our NLP model has achieved an impressive accuracy of approximately 90% in identifying electronic parts parameters, ensuring highly reliable and efficient product searches.
Technologies
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Services
Expertise
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