Extract Language

FOUNDATION AI

Automates document classification, content extraction, text analysis and data entry into downstream software, storage, and RPA systems.

Models can be called individually, or assembled and orchestrated with Extract Vision and Learning models to configure customized solutions to your business challenges.

Natural Language Processing (NLP)
Natural Language Generation (NLG)
Document Classification
Text Extraction
Speech-to-Text
Text Analysis
Topic Modeling
Sentiment Analysis

Intelligent Document Processing

Identify and extract content from virtually any type of document.

Unlike most OCR systems that require new templates for each change in form, Extract leverages pattern recognition to categorize documents based on their contents rather than their format, and AI to extract out business critical information.

It can process hundreds of documents out of the box—including common insurance documents like ACORD forms, IRS 1040s and W-2, and many types of unstructured medical reports and legal pleadings—and be quickly configured to learn new document types. Extracted information and insights can be routed to RPA bots, entered directly into enterprise systems, and analyzed and operationalized by Extract Learning models.

Leverage extracted data to derive actionable business insights.

Extract’s text analysis models can distill meaningful insights from mountains of unstructured customer data. Foundation AI can apply text analysis models to any large body of textual information, including emails, call center logs, customer reviews and social media streams.

For a large multinational appliance maker, Extract captures customer and competitor product reviews and social media mentions and applies topic and sentiment analysis models to understand customer preferences. These actionable recommendations have direct impact on product development and marketing decisions.

Customer Insight Mining

Automatic Email Routing

Get the Right Information to the Right Place, Right Away.

Extract leverages machine learning and pattern recognition to classify emails and correspondence, and text analysis and extraction to capture key information. Foundation AI can integrate Extract to enter data directly into most systems of record, and route the correspondence to the appropriate team members.

Extract has helped forward thinking enterprises re-imagine their email-driven processes, enabling reductions in response time, increased NPS, and a sustainable competitive advantage over their competitors.

Confidently search, tag, and review massive documents.

Extract includes purpose-built UI for reviewing, tagging, and annotating large PDF documents. Legal practitioners leverage this software to review massive troves of unstructured documents to identify specific words, contents, and topics germane to litigation.

Unlike most E-discovery solutions, Extract assigns confidence scores to each page and each word it processes, alerting reviewers of any low-quality sections that they should manually review, ensuring that nothing gets missed.

Document Review

Healthcare NLP

Extract Structured Data from Healthcare Records

The Foundation AI Team are pioneers in healthcare data extraction and natural language processing. The team has published several papers on deep learning methods for automatic coding of clinical freetext, and put these models in production with healthcare providers around the globe.

Their prior company built Querent which extracted over 300 billion data elements from over 36 million patients’ charts and leveraged this data to power hundreds of healthcare predictive models.