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Intelligent Document Search Engines

Combining Search with Natural Language Processing and Machine Learning

As 80% of all enterprise data is unstructured (documents, reports, images, emails, etc.), the ability to process, analyze, and unlock actionable insights within this data is key to better decision-making and business outcomes. 

Intelligent document search engines incorporate AI techniques, such as natural language processing (NLP), machine learning (ML), and semantic search, to process and analyze documents in order to extract meaning and knowledge from their content. Document search can go beyond just keyword search to provide your users with the ability to get answers to natural language queries and derive business insights from document content. Intelligent document search engines can handle a wide range of enterprise documents, from paper mails to complex documents including policies, contracts, financial, and legal agreements.


Leveraging our search, NLP, and ML expertise, we can help your organization design and implement intelligent document search engines that support various business functions. Below are some of the business documents for which we’ve helped clients build search and analytics solutions. These use cases can be implemented with the search engine of your choice, whether it’s an open source (Solr, Elasticsearch) or commercial search engine (Sinequa, Coveo, Google Cloud Search, Azure Search, Amazon CloudSearch, etc.).

  • Contracts
  • Legal agreements
  • Financial documents
  • Resumes or employee profiles
  • Business reports and presentations
  • Company policies
  • Customer reviews and social posts

In addition to search, NLP and ML can be leveraged to analyze the document content to improve automation, such as automatically routing specific document types to the corresponding departments, identifying variations of risky contract terms, recommending documents of similar themes, etc. 


intelligent document search engines
  • Content acquisition: acquiring documents and other organizational data from multiple business systems using our secure connectors designed for unstructured / semi-structured repositories.  
  • Legacy content acquisition: for content that is not already digitized, such as scanned paper documents, PDF files, or images, OCR is used to convert them into editable and searchable data.
  • Text analytics: analyze documents to identify specific language and terms. Using text analytics, we can identify sections in a document that could be useful and tag each with its purpose.
  • Information extraction: identify required information for the document using both business rules and NLP and machine learning to provide confidence levels which can be used to help decision making. 

- Using rules and natural language processing: to identify potential instances of the required information within the text.
- Machine learning: train and use a machine learning model to identify the required information within the text. The outputs of the rule-based and NLP approach can be used to train the machine learning model.

  • Deterministic classification: a pattern-based classifier can be used to look for sequences of terms which indicate a specific type of document.
  • Machine learning classification: a machine learning model can be trained to predict a specific document type and meaning.
  • Continuous learning: implement a feedback loop to continually refine the machine learning model for higher accuracy.


Experienced with multiple technology tools for handling unstructured data, we can help you evaluate, design, and build a custom, intelligent document search application for your requirements. We also leverage a range of existing technology assets to help fill the gaps and accelerate your project completion.

Contact us to see how we can help your organization build an intelligent document search engine for better insight discovery.