List of Google Natural Language AI Customers
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Since 2010, our global team of researchers has been studying Google 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 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 Natural Language AI for Natural Language Processing include: KATADATAcoid, a Indonesia based Media organisation with 220 employees and revenues of $20.0 million, Genero Australia, a Australia based Media organisation with 30 employees and revenues of $12.0 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 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 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 | Insight Source |
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Genero Australia | Media | 30 | $12M | Australia | Google Natural Language AI | Natural Language Processing | 2019 | n/a | In 2019 Genero Australia prototyped sentiment-analysis using Google Natural Language AI as part of a broader platform modernization onto Google Cloud. Genero Australia used Google Natural Language AI within the Natural Language Processing category to analyze creative submissions and learn client preferences for media and content production workflows. The prototype relied on AutoML Natural Language to surface sentiment signals and preference patterns, implementing core Natural Language Processing capabilities such as sentiment analysis, entity extraction, and model training for bespoke classification of creative assets. Functional use cases included filtering creative submissions, tagging content for client fit, and producing NLP derived insights intended to feed creative selection decisioning. Deployment was executed on Google Cloud and was led from Genero operations including Australia, operating as a prototype phase within the company platform modernization program. Governance remained with operations teams who accelerated iterative feature deployment, and the program produced plans to automate creative selection workflows using outputs from Google Natural Language AI. The implementation connects Genero Australia, Google Natural Language AI, Natural Language Processing and media and content production business functions in a single operational narrative. | ||
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iGenius Italy | Professional Services | 80 | $2M | Italy | Google Natural Language AI | Natural Language Processing | 2016 | n/a | In 2016, iGenius Italy implemented Google Natural Language AI to create Crystal, a virtual marketing advisor that answers natural-language marketing questions. The deployment targeted the marketing domain and was demonstrated at Cannes Lions in June 2016. Google Natural Language AI was used as the primary inference engine within a Natural Language Processing implementation, and the solution integrated Google Cloud Natural Language API alongside Speech-to-Text and Translation APIs. Functional capabilities implemented included entity extraction, sentiment and intent analysis, and multilingual query handling, with a lightweight application layer orchestrating requests to the Google APIs. Development and rollout were executed in Italy, producing a working prototype in 30 days and following an iterative prototyping governance model focused on rapid tuning and user feedback. The implementation scaled to thousands of users daily and delivered faster insights and approximately 50% infrastructure cost savings as reported by the project source. | ||
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KATADATAcoid | Media | 220 | $20M | Indonesia | Google Natural Language AI | Natural Language Processing | 2024 | Devoteam | In 2024, KATADATAcoid implemented Google Natural Language AI to automate news summarization and sentiment analysis for its newsroom. The deployment addressed the Natural Language Processing category and was built on Google Cloud technologies including Vertex AI and Gemini. Google Natural Language AI was configured to deliver automated article summarization, sentiment tagging, and entity extraction as core functional modules. The solution combined Google Cloud Natural Language API capabilities with Vertex AI orchestration and Gemini model augmentation to standardize analysis pipelines and enable both batch and near real time inference for editorial content. Operational scope was focused on KATADATAcoid newsroom and media functions in Indonesia, integrating analysis into editorial content pipelines and publishing workflows. The implementation was sized to scale to process over 10,000 articles per day and reduced end to end media-analysis processing time from about three hours to approximately 15 minutes. Devoteam served as the implementation partner, leading configuration, model tuning, and rollout coordination for KATADATAcoid's newsroom. Governance and workflow updates embedded automated sentiment tagging into editorial review and publication processes, delivering faster, more consistent tagging and improved editorial productivity. |
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