AI and Enterprise Search

Natural Language Processing (NLP)

Analyze and understand natural language queries, allowing users to search using conversational language instead of specific keywords, and making search more user-friendly and intuitive.

Relevance Ranking

Rank search results based on relevance, considering various factors like user behavior, document content, and context. This ensures that the most pertinent information is displayed first.

Semantic Search

Go beyond simple keyword matching and deliver results based on the meaning and context of words, providing more accurate results.

Personalization

Personalize search results based on a user’s preferences, past behavior, and profile. This tailors the search experience, making it more relevant to individual users.

Recommendation Engines

Suggest related content, products, or documents based on a user’s search queries. This is especially valuable in eCommerce and content management systems.

Content Summarization

Automatically generate summaries of documents, making it easier for users to quickly grasp the key points without reading lengthy documents.

Entity Recognition

Identify and tag entities (such as names, dates, and locations) in documents, making it easier to search for specific entities within a large dataset.

Natural Language Generation (NLG)

NLG can generate descriptive and informative snippets for search results, providing users with context and insights at a glance.

Content Classification and Taxonomy

Automatically classify and categorize content, creating a structured taxonomy that makes it easier to navigate and search for specific information.