List of Microsoft Azure Text Customers
Redmond, 98052-6399, WA,
United States
Since 2010, our global team of researchers has been studying Microsoft Azure Text 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 Microsoft Azure Text 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 Microsoft Azure Text for Natural Language Processing include: Microsoft, a United States based Professional Services organisation with 221000 employees and revenues of $243.00 billion, City of Corona, a United States based Government organisation with 2000 employees and revenues of $337.0 million, LaLiga, a Spain based Media organisation with 700 employees and revenues of $170.0 million, PharmCube China, a China based Life Sciences organisation with 300 employees and revenues of $40.0 million and many others.
Contact us if you need a completed and verified list of companies using Microsoft Azure Text, 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 software purchases.
The Microsoft Azure Text 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 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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City of Corona | Government | 2000 | $337M | United States | Microsoft | Microsoft Azure Text | Natural Language Processing | 2017 | Redapt, formerly Attunix |
In 2017, the City of Corona implemented Microsoft Azure Text in a municipal chatbot deployment to support citizen services, using Natural Language Processing to surface municipal information and respond to resident inquiries. The deployment centered on a QnA Maker chatbot built by Redapt formerly Attunix, designed for public sector citizen services and CRM use cases in the United States.
The implementation combined QnA Maker knowledge base configuration with Azure Text Analytics capabilities to interpret citizen queries, support intent classification, and extract topical entities from free text. Microsoft Azure Text was used to index municipal content and drive question and answer retrieval, enabling automated responses to common requests for information about city services, permits, and schedules.
Integrations focused on routing citizen queries into municipal workflows and complementing existing contact center operations, with the chatbot providing an upfront layer to reduce inbound call volume and speed access to municipal information. Redapt formerly Attunix led the build and deployment, including knowledge base population and training of Natural Language Processing components for city specific terminology and FAQs.
Operational governance included knowledge base curation processes and ongoing content updates to maintain response accuracy, aligning chatbot outputs with municipal information sources and citizen services teams. The deployment explicitly reduced call volumes and accelerated resident access to information, outcomes noted in the project case study.
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LaLiga | Media | 700 | $170M | Spain | Microsoft | Microsoft Azure Text | Natural Language Processing | 2018 | n/a |
In 2018, LaLiga deployed Microsoft Azure Text to power a multilingual conversational virtual assistant focused on fan engagement. The project applied Natural Language Processing to parse Spanish and English fan queries and to support customer engagement and CRM workflows.
The implementation combined Azure Bot Service for conversational orchestration, Azure App Service for hosting and scale, and Azure Text Analytics as the Microsoft Azure Text component for intent and language parsing. Configuration work included language detection, intent classification models, and conversational flow design tailored to sports fan queries.
Operational scope concentrated on fan engagement and self-service in Spain, with the assistant engineered to scale to millions of daily interactions across digital channels. The solution routed queries into self-service experiences and support workflows, shifting routine query handling toward automated conversational handling for customer engagement teams.
Governance and rollout aligned operational ownership to fan engagement and CRM teams and followed the cloud-first deployment patterns documented in the Microsoft Azure case study. The published case study reports improved fan engagement and expanded self-service capability driven by Microsoft Azure Text and the broader Azure conversational AI stack.
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Microsoft | Professional Services | 221000 | $243.0B | United States | Microsoft | Microsoft Azure Text | Natural Language Processing | 2019 | n/a |
In 2019, Microsoft deployed Microsoft Azure Text in the finance organization to power an AI driven chatbot for the Procure to Pay service. Microsoft Azure Text is classified as Natural Language Processing and was embedded by Microsoft Finance Engineering within Core Services Engineering and Operations to create a conversational layer for procurement and payment workflows.
The implementation consolidated 16 discrete services that composed the Procure to Pay platform into a services oriented architecture with a singular end to end user experience layer. Functional modules included conversational intent detection and context management, QnA based knowledge retrieval, cognitive text analytics and bot orchestration, implemented using Azure Bot Service, Microsoft LUIS, QnA Maker, and Microsoft Azure Text analytics alongside broader Azure Cognitive Services.
System architecture integrated real time and big data pipelines for telemetry and insights, using Application Insights and Kusto for live site monitoring, Azure Stream Analytics with Azure Event Hubs for event ingestion, and Azure Databricks with Azure Data Lake Storage for batch and stream processing. Back end services were exposed through microservices hosted on Azure Service Fabric, and the solution accepted multi channel user interaction including Cortana, enabling the chatbot to perform actions by abstracting underlying API and master data complexity.
Governance and process changes emphasized a user centric, AI first approach that shifted focus from siloed applications to a unified conversational service for Procure to Pay. The stated objectives were to simplify the user experience, reduce clicks, enable fluid conversational handoffs across contexts, and automate or eliminate manual process tasks where appropriate, while preserving analytics and monitoring for ongoing operations.
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Life Sciences | 300 | $40M | China | Microsoft | Microsoft Azure Text | Natural Language Processing | 2022 | Microsoft |
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