List of IBM Watson Language Translator Customers
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Since 2010, our global team of researchers has been studying IBM Watson Language Translator 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 IBM Watson Language Translator 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 IBM Watson Language Translator for Natural Language Processing include: Santander Bank US, a United States based Banking and Financial Services organisation with 17200 employees and revenues of $9.00 billion, Easter Seals Central Texas, a United States based Non Profit organisation with 100 employees and revenues of $9.0 million, Lingmo International, a Australia based Professional Services organisation with 20 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using IBM Watson Language Translator, 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 Artificial Intelligence software purchases.
The IBM Watson Language Translator 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 Artificial Intelligence 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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Easter Seals Central Texas | Non Profit | 100 | $9M | United States | IBM | IBM Watson Language Translator | Natural Language Processing | 2018 | n/a |
In 2018, Easter Seals Central Texas implemented IBM Watson Language Translator to support beginning Spanish language experiences for its client-facing learning tools. The IBM Watson Language Translator deployment used Natural Language Processing to provide translation, language detection, and multilingual content delivery capabilities integrated into web sessions.
The implementation combined a client-side stack using AJAX, HTML, CSS, JavaScript and the Angular and Bootstrap frameworks with a Node.js server layer that exchanged JSON and XML payloads. Data persistence and serverless logic leveraged Google Cloud Firestore and Google Cloud Functions, while speech and additional translation components were integrated using IBM Watson Text to Speech, Google Text to Speech and Google Translate, and lexical lookups used the Oxford English Dictionary API.
Operational scope covered the organization’s web-based language learning modules and internal staff-facing tools across the United States, with IBM Watson Language Translator serving as the core Natural Language Processing service for translation workflows. Governance emphasized modular service orchestration and API-driven integration patterns to enable iterative enhancements and to deliver the enhanced beginning Spanish language experience.
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Lingmo International | Professional Services | 20 | $2M | Australia | IBM | IBM Watson Language Translator | Natural Language Processing | 2016 | x |
In 2016, Lingmo International implemented IBM Watson Language Translator to adopt a cognitive approach to its language services and accelerate model development. The deployment targeted enhancement of speech recognition and faster model training workflows for a small professional services firm based in Australia, leveraging Natural Language Processing capabilities for speech-to-text, translation, and model customization. IBM Watson Language Translator was provisioned as a cloud cognitive service and accessed via API to support near real-time transcription and translation within Lingmo's product stack.
IBM Watson Language Translator was configured to support domain-specific model customization using Lingmo's proprietary corpora, enabling training iterations with relatively small volumes of data. Functional capabilities implemented included speech recognition enhancement, language model training, translation inference endpoints, and automated retraining pipelines to shorten iteration cycles. The architecture emphasized API-driven processing, cloud-hosted models, and integration at the speech ingestion and translation inference layers to operationalize Natural Language Processing workflows.
Operational scope centered on product development and engineering teams responsible for dataset curation, model versioning, and iterative testing, with governance changes to accommodate faster training cadences and model lifecycle management. Lingmo switched to a cognitive approach using IBM Watson Language Translator which enabled more rapid model training even with limited data, and process adjustments focused on streamlining annotation, testing, and deployment of updated translation models.
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Santander Bank US | Banking and Financial Services | 17200 | $9.0B | United States | IBM | IBM Watson Language Translator | Natural Language Processing | 2018 | n/a |
In 2018, Santander Bank US implemented IBM Watson Language Translator as a targeted Natural Language Processing deployment to standardize multilingual content and enable language normalization across digital touchpoints. The implementation is described within an IBM Watson centric environment and aligns the IBM Watson Language Translator with the bank's NLP tooling and platform components.
The IBM Watson Language Translator implementation was configured to operate alongside Watson Assistant and IBM Discovery as part of a broader language processing pipeline. Functional capabilities focused on translation, terminology normalization and text preprocessing, with IBM Db2 11.5 used as a structured terminology and metadata store and IBM Bluemix Storage holding corpora and model artifacts.
Integrations are explicit and API driven, using Swagger for service contracts and Kubernetes v1.14 for container orchestration of translator services. Change and reporting workflows linked the implementation to Jira v8.x under a Scrum framework for release cadence and Tableau v9.0 for visualization and usage reporting, and Ebm core was included in the environment to support processing orchestration.
Operational governance emphasized iterative delivery through Scrum, with Jira tracking backlog and defects, and container lifecycle managed on Kubernetes to support continuous updates to IBM Watson Language Translator models. API specifications governed cross team consumption, and Db2 plus Bluemix Storage provided the data foundation for model training and runtime lookups.
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