List of Conversus.AI Customers
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United States
Since 2010, our global team of researchers has been studying Conversus.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 Conversus.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 Conversus.AI for Natural Language Processing include: Walmart, a United States based Retail organisation with 2100000 employees and revenues of $681.00 billion, Johnson & Johnson, a United States based Life Sciences organisation with 138100 employees and revenues of $88.82 billion, Mattel, a United States based Retail organisation with 34000 employees and revenues of $5.38 billion and many others.
Contact us if you need a completed and verified list of companies using Conversus.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 Conversus.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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Johnson & Johnson | Life Sciences | 138100 | $88.8B | United States | Converseon | Conversus.AI | Natural Language Processing | 2018 | n/a |
In 2018, Johnson & Johnson implemented Conversus.AI for Natural Language Processing to analyze social data and support marketing and corporate reputation work in the United States. The vendor platform describes the deployment as leveraging social listening and AI to surface public perception and the impact of brand purpose efforts, aligning the application to corporate communications and marketing intelligence use cases.
The implementation used Converseon's PRISM capability alongside Conversus NLP to provide category-aligned functionality such as sentiment analysis, topic extraction, entity recognition and trend detection across social channels. Conversus.AI was operated as an analytics and insight layer, ingesting social data streams and applying Natural Language Processing pipelines to convert unstructured social content into thematic and sentiment signals.
Operational scope centered on US-based marketing and corporate communications teams, with outputs feeding reputation monitoring and messaging decisions. Governance emphasized analytic workflows and attribution of social signals to brand purpose initiatives as described by the vendor, and the source does not specify additional third party system integrations.
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Mattel | Retail | 34000 | $5.4B | United States | Converseon | Conversus.AI | Natural Language Processing | 2019 | n/a |
In 2019, Mattel implemented Conversus.AI to apply Natural Language Processing to its social listening and customer feedback streams for corporate reputation and customer loyalty in the United States. The deployment positioned Conversus.AI within marketing and customer experience functions to generate structured intelligence from unstructured social data.
The implementation leveraged Conversus NLP and custom models to perform sentiment analysis, entity extraction, topic modeling, and automated classification, producing normalized tags and prioritized issue signals. Configuration emphasized model tuning for brand and loyalty related entities, with model outputs surfaced in analyst dashboards and alerting workflows to accelerate decisions.
The solution ingested social listening feeds and operationalized outputs for marketing campaign teams and customer experience managers across the United States, creating a consistent source of reputation signals and loyalty indicators. Integrations focused on data ingestion pipelines and downstream operational workflows that routed tagged conversational insights to business users.
Governance established tagging standards and escalation workflows to convert conversational signals into actionable intelligence for corporate reputation monitoring and customer loyalty initiatives, aligning reporting for marketing and executive stakeholders. Vendor materials from Converseon cite that their NLP and applied AI approach helped Mattel transform social listening data into actionable intelligence for those business functions.
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Walmart | Retail | 2100000 | $681.0B | United States | Converseon | Conversus.AI | Natural Language Processing | 2017 | n/a |
In 2017, Walmart engaged Converseon's Conversus.AI in a Natural Language Processing implementation to extract text based insights from Tweets and to support social listening and analytics work in the United States. Conversus.AI was positioned to provide NLP and text analytics capabilities aimed at reducing developer effort for text analytics while feeding downstream social analytics workflows. The statement links Walmart Conversus.AI Natural Language Processing to business functions including social listening, customer insights, and analytics operations.
The implementation centered on Conversus.AI text analytics models as described by the vendor, with functionality aligned to category standard capabilities such as sentiment analysis, entity extraction, topic classification, and automated tagging to standardize analyst inputs. Configuration and model tuning were applied to retail oriented language and to pipelines that processed streaming and historical Tweet content, producing structured annotations for consumption by analytics teams. These modules and model outputs provided the core text analytics capability within the Conversus.AI deployment.
Integration with the Twitter feed was explicit, ingesting Tweets into the Conversus.AI processing pipeline for analysis and annotation. Processed outputs were consumed by Walmart social listening and analytics workflows operating in the United States, enabling centralized analysis of public social data. Operational coverage focused on social analytics functions rather than broader enterprise systems.
Governance for the rollout emphasized reducing developer effort for text analytics by shifting work to model driven workflows and standardized tagging schemas delivered through Conversus.AI. The implementation supported social listening and analytics teams, enabling more automated text analytics on Tweet content and lowering the need for bespoke developer pipelines.
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