Enterprise Search Services & Solutions Provider Company

To get potential and highly accurate enterprise search service, we assist natural language search service operated by machine learning. 100% Confidential and Secure
Successful Products

Key features of our Enterprise Search service using ML

  • Natural language & keyword support
    For accurate query results from your data, natural language search is the solution. The base of the enterprise search engine is to search as done by end-users by understanding the natural language questions with some specific keywords and common questions. And the Enterprise search will revert with the precise answer of the asked question.
  • Document Ranking
    To offer your user with more information to explore, we integrate deep learning to search for an accurate answer. Along with machine learning, we integrate semantic search models of deep learning to aggregate the searched answer and to give back the ranked list.
  • Connectors
    Connectors are much secure of your data source by maintaining document access rights for you to search the content without risk. By offering a huge range of native cloud and on-premises connectors, Openxcell helps to eliminate the heavy lifting for Dropbox, documents, database, Salesforce, and more. You can easily access the connectors that automatically sync the index with the data source.
  • Reading Comprehension & FAQ matching
    If you are looking to get accurate answers from the unstructured data, no training is needed, you have to point the enterprise search engine to content, and natural language search will identify answers from the relevant document. In another way, you can input an FAQ directly to the enterprise search engine to get answers for most common questions asked by end-users.
  • Domain Optimization
    With the help of deep learning models for huge internal use cases like operations, R&D, domain, insurance, media and entertainment, travel and hospitality, oil and gas, health, HR, news, telecommunications, mining, food and beverage, and more, we understand natural language queries better and document content and other structures.
  • Relevance Tuning
    On the basis of view count or vote, Enterprise search facilitates documents boosting that is common in forums and other support type knowledge bases. To assign more importance to a specific response, you can also boost some fields in your index and search engines allow you to tune for specific data sources.
  • Incremental Learning
    We retrain our machine learning models regularly by capturing the end-user search patterns and feedback for customers. This is not enough to win the race. Still, more recent documents can be ranked higher in the list. Additionally, for the new documents to be at the top, machine learning models will learn to increase priority.
  • Query auto-completion
    An auto-complete feature of our Enterprise search engine includes an end user’s search query. A query that is answered by auto-completion helps the end-user to get query results in the search bar by almost 70% of writing. So no need to write a full query as all commonly asked questions will be answered precisely.

Benefits of Using Enterprise Search Service

  • Ask natural language questions, get immediate answers
    To get the exact answers of all your queries, no need to shift the link, use our natural language questions instead of simple keywords. Either for FAQ, any precise answers from the dataset, or for the whole document, our machine learning-based enterprise search will assist for all.
  • Bring all of your data together
    Get the benefit of Enterprise search to rid of information silos. Add content with ease to a centralized location for a fast search result and accurate answer to your queries from file documents, SharePoint, intranet sites, file-sharing services, and more.
  • Constantly improving search results
    We update our machine learning models algorithms to get the relevant and accurate results over time search for our end users. Additionally, you can add your fine-tune results manually on your own by adjusting the importance of certain data sources.