developed a proprietary, continuously improving,
concept-based search engine, which uses deep learning to
understand the semantic content of queries and
documents, and it learns from user behavior to
continually improve over time.
Most search engines today, including popular consumer-facing and enterprise search engines such as Google and Elastic Search, are based on keywords - the search engine finds documents containing the keywords in a user's query, using simple statistics like TF-IDF to find documents that feature those keywords frequently or in close proximity to each other within the document. While keyword search can return helpful results, the process does not consider the context of the user's questions. Consequently, the optimal result may never get returned since the search engine is simply attempting to match keywords from the search query with search results.
semantic search model for information retrieval has
proven to outperform all other traditional methods on
all benchmarks. Based on numerous live experiments
evaluating our model across hundreds of thousands of
documents, we've seen that the method tied or
outperformed the traditional keyword method 98% of the
time and was over 5 times more likely to return a result
that matched what they user was looking to retrieve.
Subscribe for the latest!
Don't miss news on conversational AI, automation, and our latest announcements