List of Native AI Customers
New York, 10013, NY,
United States
Since 2010, our global team of researchers has been studying Native 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 Native AI for Digital Twin 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 Native AI for Digital Twin include: The Manual, a United States based Media organisation with 26 employees and revenues of $4.0 million, Parade Publications, a United States based Media organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using Native 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 Native 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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Parade Publications | Media | 10 | $1M | United States | Native AI | Native AI | Digital Twin | 2016 | n/a | In 2016, Parade Publications implemented Native AI on its website. Parade Publications implemented Native AI as a Digital Twin to model content and audience interactions and to support editorial and audience engagement workflows. For a 10 person media organization based in the United States the deployment is focused on site level embedding and runtime inference rather than extensive enterprise orchestration. Native AI is configured to operate as a Digital Twin with capabilities aligned to content personalization, audience modeling, and recommendation logic, and it is applied within the publication content delivery pipeline and editorial workflow. The implementation emphasizes lightweight configuration and front end integration with the website content management and analytics processes, with governance centered on editorial review and content control flows. This site centric Digital Twin deployment positions Native AI to inform editorial decision making and shape on site audience experiences without disclosure of named third party system integrations. | |
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The Manual | Media | 26 | $4M | United States | Native AI | Native AI | Digital Twin | 2017 | n/a | In 2017, The Manual implemented Native AI on its website, deploying the Native AI application as a Digital Twin to augment online content interactions. The deployment established a content layer that models audience behavior and enables simulated interactions and conversational interfaces tied to editorial pages. The Manual Native AI Digital Twin supports editorial and audience engagement functions and is embedded directly into the public site. The implementation used cloud hosted processing with client side embedding into the site front end, configured to surface contextual recommendations and persona driven content simulations across articles and guides. Functional modules implemented included content modeling, conversational query handling, and automated content tagging to align simulated audience signals with editorial workflows. Operational scope focused on editorial and product teams, with governance centered on content quality assurance, moderation, and integration of the Digital Twin outputs into existing publishing workflows. |
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