Document Analysis Applications for Business
Tapping into Hidden Insights in Your Unstructured Content
DOCUMENT ANALYSIS APPLICATIONS IN BUSINESS
Enterprise document analysis applications use AI techniques including Natural Language Processing (NLP), entity extraction, semantic understanding, and machine learning to analyze content and extract meaning and knowledge.
- Interpret legal and financial documents to identify risks and non-compliance. For instance, a document analysis application can automatically examine text documents, such as policies, contracts, and legal agreements, to understand the content and identify risky language that may pose a risk to the business.
- Automatically analyze, sort, and route incoming mail to the corresponding departments within the organization.
- Extract excerpts from various content sources on a particular topic or theme to automatically create a new piece of content (e.g. a newsletter).
- Automatically identify and match candidates to jobs to improve recruiters’ productivity and fill rates
- Automatically identify and categorize documents for migration to lower-cost storage, archival, or removal, allowing for storage cost reduction and a 360-degree view into organizational data
- Improve compliance and risk management
- Internal operational efficiencies
- Enhance business processes and automation
OUR DOCUMENT ANALYSIS APPROACH
- Documents can be acquired from multiple sources using secure connectors. For legacy paper documents, Optical Character Recognition (OCR) can convert different types of documents, such as scanned paper documents, PDF files, or images into editable, sortable, and searchable data.
- Cleanse, normalize, and enrich documents to a consistently high standard, enabling search and analytics applications to perform optimally.
- Use natural language processing (NLP) techniques to identify specific pieces of information, such as date, order number, or policy number in documents that have been digitized. The resulted data can then be categorized for a multitude of business use cases, including insight discovery and automating processes to improve efficiency.
- Text analysis: analyzes documents to identify specific language or terms and extract linguistic meaning
- Deterministic classification: a Pattern-Based Classifier can be used to look for sequences of terms which indicate a specific sort of document
- Machine learning: trains a machine learning model with example data sets to predict document type or extract and classify text (e.g. learning aircraft component name)
- Search and analytics capabilities can be integrated to help enterprise users find and analyze information faster and easier.
TECHNOLOGY ASSETS TO SUPPORT DOCUMENT ANALYSIS APPLICATIONS
- Content acquisition – acquiring text documents and other types of organizational data from multiple business systems with secure connectors
- Unstructured content processing – Aspire content processing framework for efficient processing of unstructured documents
- Natural Language Understanding (NLU) framework – a scalable, cost-effective, easy-to-use framework that processes and understands complex business documents and user queries. Learn more about Saga NLU framework
- Unified search and/or analytics UI – providing users an enhanced insight discovery experience
Contact us to discuss how document analysis applications can improve insight discovery and your business outcomes.