List of Google Cloud Natural Language AI Customers
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Since 2010, our global team of researchers has been studying Google Cloud Natural Language AI 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 Google Cloud Natural Language AI 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 Google Cloud Natural Language AI for Natural Language Processing include: Hearst, a United States based Media organisation with 22000 employees and revenues of $12.00 billion, Velip Brazil, a Brazil based Communications organisation with 25 employees and revenues of $5.0 million, iGenius Italy, a Italy based Professional Services organisation with 80 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using Google Cloud Natural Language AI, 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 Google Cloud Natural Language AI 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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Hearst | Media | 22000 | $12.0B | United States | Google Cloud Natural Language AI | Natural Language Processing | 2018 | n/a |
In 2018, Hearst Newspapers deployed Google Cloud Natural Language AI to automatically classify and tag editorial content across its US newspaper properties, enabling real-time categorization of approximately 3,000 articles per day. This implementation leverages Natural Language Processing capabilities to standardize content taxonomy and drive automated tagging of articles for downstream personalization and advertising use cases.
The implementation centered on a content-classification pipeline that extracted entities and topical categories from article text, and applied automated tagging rules to attach category and entity metadata. Google Cloud Natural Language AI was used as the analytic engine for classification and entity extraction, integrated into Hearst's ingestion and content processing workflows to support near real-time tagging and metadata enrichment.
Integration points included forwarding category and entity data into Hearst’s customer data platform and BigQuery analytics for use by personalization engines and ad-targeting systems. The deployment covered more than 30 media properties within Hearst Newspapers and was scoped to editorial content processing pipelines, aligning content metadata with audience and advertising workflows.
Operational changes included shifting manual tagging efforts to an automated pipeline and embedding category/entity outputs into downstream personalization and advertising workflows. The project explicitly reduced manual tagging effort, improved content personalization and ad targeting, and enabled analytics and segmentation in BigQuery through the enriched metadata produced by Google Cloud Natural Language AI.
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iGenius Italy | Professional Services | 80 | $2M | Italy | Google Cloud Natural Language AI | Natural Language Processing | 2016 | n/a |
In 2016, iGenius Italy implemented Google Cloud Natural Language AI to build Crystal, an AI marketing advisor that answers business questions in natural language. The implementation used Google Cloud Natural Language API together with Speech-to-Text and Translation APIs to ingest spoken queries, normalize multilingual inputs, and extract entity and sentiment signals for question answering, with the working prototype delivered within 30 days.
The deployment architecture centered on Google App Engine for application hosting and request orchestration, while Natural Language Processing capabilities were provided by Google Cloud Natural Language AI and complementary speech and translation services. Operational scope focused on marketing use cases, where Crystal served as an advisor answering business questions for marketing teams, and the project reported faster time-to-market and approximately 50% server cost savings while running on Google App Engine.
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Velip Brazil | Communications | 25 | $5M | Brazil | Google Cloud Natural Language AI | Natural Language Processing | 2018 | n/a |
In 2018 Velip Brazil implemented Google Cloud Natural Language AI to power conversational voice and video bots for customer service and contact-center automation, establishing Natural Language Processing as a core component of its customer engagement stack. The migration began in January 2018 and by May 2018 the deployment delivered lower latency and met a higher availability target for contact center workloads.
The implementation centered on the Google Cloud Natural Language API integrated with Dialogflow and Speech-to-Text, routing streaming audio through Speech-to-Text for real time transcription, then invoking Dialogflow for intent handling and the Google Cloud Natural Language API for richer entity extraction and semantic understanding. Configuration work included intent modeling, entity schema design, and conversational turn control to support both voice and video bot interactions, aligned with common Natural Language Processing functional workflows such as intent classification and entity recognition.
Integrations were explicit and tightly scoped, Dialogflow and Speech-to-Text were used alongside Google Cloud Natural Language API to create an end to end conversational pipeline, and the solution was applied across customer service and contact-center automation functions in Brazil. Operational coverage focused on inbound voice and video channels, with runtime orchestration in cloud APIs and real time transcription feeding the NLU layer to reduce response latency.
Rollout was phased, with initial testing and tuning in early 2018 followed by wider production rollouts by May 2018, accompanied by monitoring for SLA and latency. Outcomes reported by May 2018 included a jump in SLA from 99% to 99.9%, approximately 50 percent reduction in voicebot response time, and approximately 30 percent infrastructure cost savings, reflecting both performance and cost changes observed during the implementation.
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