Amazon Comprehend Customers and Partners

  • Assent

    We help companies by providing transparency, traceability and real understanding of their supply chain data so they can protect their brands, remove market access barriers, and reduce operational and financial risk.

    We strive to combine technology and business domain expertise to help our customers understand compliance risks in their supply chain. We needed a way to process compliance documents at scale. Our process is to read images and PDF documents with forms, tables, and free-form text and extract data of interest from within those documents. Amazon Textract's OCR technology enabled us to extract text from documents. Amazon Comprehend's context-aware NLP APIs extracted business-specific entities and their values from the text. We also incorporated humans in our workflow using Amazon Augmented AI (Amazon A2I), to have our teams review extracted data and provide feedback to the ML models and help improve them over time. Using this efficient mix of human and machine learning along with AppSync and Amplify provided us more accurate insights into our customers' supply chain risk and saved them hundreds of hours in manually reviewing documents. They can now get immediate feedback on whether their company is at compliance risk.

    Corey Peters, AI/ML Team Lead, Assent Compliance
  • ExxonMobil

    The need for energy is universal. That's why ExxonMobil is pioneering new research and pursuing new technologies to reduce emissions while creating more efficient fuels and lubricants. ExxonMobil is committed to responsibly meeting the world's energy needs. 

    AWS and Amazon Business digital implementations into ExxonMobil's procurement organization is enhancing its global operations and preparing them for unexpected disruptions.  "We’ve worked with the Amazon ML Solutions Lab to develop a proof of concept aimed at maximizing contract utilization and further reduction of costs. One approach leverages Amazon SageMaker to improve identification of best-matched catalog items from free text entries in ExxonMobil’s eProcurement system, Smart by GEP. When catalog item descriptions are not readily available, we use Amazon Comprehend to create a bespoke classification model to map free text entries to supplier contract agreements.

    Mariano Matzkin, Global MRO Procurement Manager, ExxonMobil
  • FINRA

    FINRA is a not-for-profit organization dedicated to investor protection and market integrity. It regulates one critical part of the securities industry – brokerage firms doing business with the public in the United States.

    FINRA receives millions of documents with unstructured data to support investigative, examination, and compliance processes. Our investigators and examiners had to manually go through documents page by page or run very targeted searches to find what they needed. With Amazon Comprehend, we can quickly extract individuals and organization, match extracted entities to FINRA records, flag individual of interest, and detect similarities with other documents.

    Dmytro Dolgopolov, Senior Director of Technology of FINRA
  • HM Land Registry (HMLR)

    Using the natural-language processing capabilities of Amazon Comprehend, the application can extract meaning from complex legal language, identify minor differences, and flag issues for caseworkers to review. By offloading manual work from caseworkers, who previously needed to compare thousands of documents each week, HMLR has doubled its document review speed and can approve property transfers faster. This solution also reduces the risk of indemnity claims: it flags discrepancies early in the application process, prompting caseworkers to resolve issues before they evolve into legal disputes. HMLR deployed a web application to automate document comparison, cutting review time by 50 percent and increased staff productivity.

    Read the case study

  • LexisNexis

    LexisNexis Legal & Professional is a global provider of content and technology solutions for legal and business professionals, serving customers in more than 175 countries, offering over 2 billion searchable archives.

    We provide legal professionals with insightful research and analytics to help them make informed decisions. Therefore, we are always looking for better ways to discover insights from legal documents. Thanks to Amazon Comprehend's automatic machine learning (ML), we can now build accurate custom entity recognition models without getting into the complexities associated with ML. The entities that we care about the most, such as judge and attorney, can be identified quickly from over 200 million documents at above 92% accuracy.

    Rick McFarland, Chief Data Officer of LexisNexis
  • Siemens

    Siemens built an AWS survey-response processing solution that sends completed surveys to Amazon Comprehend for language identification then to Amazon Translate to execute translations. After Amazon Comprehend anonymizes any names, Amazon SageMaker detects and organizes responses into categories and topics. In addition to returning analyzed, sorted survey results at least 75 percent faster than before, the AWS solution makes the surveying program much less expensive.

    Procuring human processing and analysis of past employee surveys cost multiple euros per interview. By using Amazon Comprehend and other AWS services, we are getting translation, processing, and analysis for less than one euro per interview.

  • Schuh

    At schuh’s support centre, the company uses Amazon Comprehend’s natural language processing (NLP) and machine learning (ML) capabilities to analyse customer emails and recognise the sentiment of the messages.The technology is so effective that it can automatically assess, for example, that 41 percent of communications contain positive or negative sentiment—long before the support team logs in. Support tickets are sorted by issue and colour-coded, then passed through to the customer care agent who can best deal with them based on experience or area of expertise. Prior to using Comprehend, prioritising queries was manual and time consuming.

    Read the case study

    Using Comprehend to put a customer problem in front of the right person really gives us the best chance of retaining that customer going forward.

     

  • Chick-fil-A

    Chick-fil-A Uses Amazon Comprehend to Help Spot Foodborne Illness

    Watch the video

  • Vision Critical

    Vision Critical provides a customer relationship intelligence software that enable large enterprises to be fast, responsive, and customer-centric.

    Our Sparq platform connects your most important customer data from any source—including transaction, attitude, emotion, and intent data—to build dynamic customer profiles that give every team and business system a unified view of the customer. By integrating with Amazon Comprehend’s sentiment analysis capability, the platform now turns qualitative customer feedback into actionable insight, determining whether their feedback is positive, negative, or neutral at over 90% accuracy.

    Nicholas Simon, Product Manager of Vision Critical
  • SuccessKPI

    SuccessKPI is an experience analytics platform empowering businesses across the world to get insights into customer experience, bring efficiency to the workforce, and ultimately drive business outcomes. Major contact centers globally across multiple CaaS vendors leverage SuccessKPI’s analytics platform.

    Understanding customer sentiment across various products or services are key to understanding the health of the business. Amazon Comprehend Targeted Sentiment allows our customers to not only understand sentiment for a conversation but also drill down to specific areas of product or business at scale.

    Praphul Kumar, Chief Product Officer, SuccessKPI
  • Gallup

    Gallup is a global analytics and advice firm that helps organizations with culture activation and enablement programs that take strategy into action to deliver improved and sustainable employee and customer engagement. Gallup Access is our proven workplace platform used for data collection, analytics, and learning to drive real change.

    We are very excited about the Amazon Comprehend Targeted Sentiment feature because it will enhance our existing open-ended survey response reporting in Gallup Access. We currently report overall sentiment related metrics and with this new feature, we will be able to provide more targeted sentiment within those survey responses. This will enhance the value proposition of our overall reporting and provide our users with more accurate and actionable data.

    Swapan Golla, Director of Analytics, Gallup
  • TINT

    TINT helps B2C marketers find, curate, and display their most effective customer generated content from social media in their marketing.

    Our business is focused on delivering the best marketing content possible for the brands that depend on us. Using Amazon Comprehend, we were able to significantly increase the quality and accuracy of our platform’s content analytics capabilities, which identifies the right content for the most impactful marketing campaigns. Amazon Comprehend allows us to focus on our core product and not worry about the heavy lifting associated with building our own machine learning models.

    Ryo Chiba, CTO of TINT
  • Vibes

    Vibes Mobile Engagement Platform enables marketers to engage one on one with today’s hyper-connected mobile consumers at scale.

    Mobile messaging connects brands and consumers in a way that is direct, personal and authentic. At Vibes, we process billions of mobile messages every month and there are deep insights latent in the vast number of messages we process. Amazon Comprehend enables us to quickly extract key phrases, detect sentiment, and model topics from unstructured message content—providing marketers with a deeper understanding of their performance and actionable insights to deliver rewarding customer experiences.

    Brian Garofola, CTO of Vibes
  • Zillow

    Zillow: Building Speech Analytics Using AWS AI Services

    Watch the video.