List of Amazon Comprehend Customers
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Since 2010, our global team of researchers has been studying Amazon Comprehend customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Amazon Comprehend for Natural Language Processing from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Amazon Comprehend for Natural Language Processing include: LexisNexis, a United States based Professional Services organisation with 11800 employees and revenues of $2.41 billion, FINRA, a United States based Professional Services organisation with 3600 employees and revenues of $1.11 billion, Fred Hutchinson Cancer Center, a United States based Healthcare organisation with 5700 employees and revenues of $941.0 million, IPC Systems, a United States based Professional Services organisation with 1400 employees and revenues of $260.0 million and many others.
Contact us if you need a completed and verified list of companies using Amazon Comprehend, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Machine Learning software purchases.
The Amazon Comprehend customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Machine Learning software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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FINRA | Professional Services | 3600 | $1.1B | United States | Amazon Web Services (AWS) | Amazon Comprehend | Natural Language Processing | 2017 | n/a |
In 2017, FINRA deployed Amazon Comprehend on Amazon Web Services to process millions of unstructured documents supporting investigative, examination, and compliance processes. FINRA implemented Amazon Comprehend as a Natural Language Processing capability to extract entities and surface document relationships for investigators and examiners.
The implementation focused on core Natural Language Processing modules, including entity extraction for individuals and organizations, entity resolution to match extracted entities to FINRA records, and document similarity detection to identify related materials across corpora. Amazon Comprehend was configured to produce structured metadata and entity flags that feed downstream review and case management workflows.
Amazon Comprehend was integrated into FINRA’s document ingestion pipeline and linked directly to FINRA records for automated matching, enabling pre-review tagging and prioritization of documents. Operational coverage explicitly includes investigative teams, examination units, and compliance analysts who rely on extracted metadata to locate relevant evidence and reduce manual search effort.
Governance changes centered on embedding entity flags into reviewer workflows so that flagged individuals of interest trigger secondary manual review and investigative follow up. As described by FINRA technology leadership, Amazon Comprehend enables the organization to quickly extract individuals and organizations, match extracted entities to FINRA records, flag individuals of interest, and detect similarities with other documents, improving the efficiency of investigative and examination processes.
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Fred Hutchinson Cancer Center | Healthcare | 5700 | $941M | United States | Amazon Web Services (AWS) | Amazon Comprehend | Natural Language Processing | 2017 | n/a |
In 2017, Fred Hutchinson Cancer Center implemented Amazon Comprehend for Natural Language Processing to accelerate clinical research workflows. The deployment focused on research teams responsible for developing clinical trials and matching patients, automating the extraction and labeling of unstructured clinical record data to reduce manual chart review time.
Implementation centered on Amazon Comprehend and Comprehend Medical capabilities for clinical entity extraction, clinical concept normalization, and automated document classification, enabling rapid annotation of free text clinical notes. The configuration emphasized automated labeling pipelines and text processing workflows that convert unstructured records into structured clinical metadata for downstream analysis. This enabled researchers to query and surface trial-relevant criteria from large volumes of notes.
The service was integrated with Fred Hutchinson data processing pipelines ingesting clinical records and research datasets, and governance included controlled annotation review workflows managed by research operations to validate automated labels before use in trial matching. According to Matthew Trunnell, Chief Information Officer, Amazon Comprehend Medical reduced the time burden from hours to seconds and provided researchers more rapid access to the information needed to advance therapies. Fred Hutchinson Cancer Center Amazon Comprehend Natural Language Processing implementation supported clinical research and trial matching business functions across the institute.
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IPC Systems | Professional Services | 1400 | $260M | United States | Amazon Web Services (AWS) | Amazon Comprehend | Natural Language Processing | 2020 | n/a |
In 2020, IPC Systems implemented Amazon Comprehend to power a sentiment analysis-backed client survey, embedding Natural Language Processing capabilities directly into its customer portal. IPC Systems Amazon Comprehend Natural Language Processing implementation was positioned to capture and analyze free-text survey responses from clients and surface sentiment signals within client-facing dashboards.
The technical implementation centered on Amazon Comprehend for sentiment analysis and text analytics, with a data pipeline that used Amazon S3 for raw and staged storage, AWS Glue for ETL and schema transformation, Amazon Athena for ad hoc SQL querying of processed text, and Amazon QuickSight for visualization. Functional modules included sentiment scoring, text classification and key-phrase extraction workflows, orchestration of ETL jobs, and dashboarding for user-facing insight delivery.
Integrations were explicitly implemented with Amazon S3, AWS Glue, Amazon Athena, and Amazon QuickSight, and the solution was deployed to publish visualizations on IPCs customer portal for clients to review survey results. The NLP pipeline operated alongside operational tooling used by the network and support organization, including Netcool monitoring and the incident ticketing workflows that service financial firms and traders internationally, enabling analysts and Tier One support to reference sentiment insights during client interactions.
Governance and adoption included embedding the insight feeds into customer-facing dashboards and leveraging internal training channels to support uptake, while operational scope covered client-facing teams and internal users responsible for support and network incident lifecycle management. The configuration emphasized reusable ETL jobs, queryable analytics artifacts, and dashboard governance to maintain consistent sentiment reporting across IPC Systems.
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Professional Services | 11800 | $2.4B | United States | Amazon Web Services (AWS) | Amazon Comprehend | Natural Language Processing | 2017 | n/a |
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Buyer Intent: Companies Evaluating Amazon Comprehend
- Belkins Ukraine, a Ukraine based Professional Services organization with 50 Employees
- L L P Godfrey Susman, a United States based Professional Services company with 180 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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