List of Meta AI Customers
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Since 2010, our global team of researchers has been studying Meta 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 Meta AI for Chatbots and Conversational AI 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 Meta AI for Chatbots and Conversational AI include: Mayo Clinic, a United States based Healthcare organisation with 80221 employees and revenues of $17.90 billion, Niantic, a United States based Media organisation with 500 employees and revenues of $1.00 billion, Foondamate, a South Africa based Education organisation with 25 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using Meta 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.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Foondamate | Education | 25 | $2M | South Africa | Meta AI | Meta AI | Chatbots and Conversational AI | 2024 | n/a |
In 2024 Foondamate adopted Meta AI for Chatbots and Conversational AI to power its education chatbot serving students via WhatsApp and Messenger. The implementation targeted student-facing tutoring workflows and education customer agent interactions across the Africa region, including South Africa and Nigeria, with the goal of improving conversational quality on low data messaging channels.
Foondamate integrated Meta AI using Llama conversational models to improve answer quality, tone adaptation and multi step tutoring interactions. Configuration emphasized conversational NLU and dialogue management capabilities typical of Chatbots and Conversational AI, including context retention for multi step lessons, adaptive response tone for different learner profiles and model tuning to align answers to education content and pedagogical voice.
The deployment tied Meta AI directly into WhatsApp and Messenger messaging channels to scale conversational throughput for both student queries and agent assisted scenarios. Operational coverage included education teams and customer agent workflows across Foondamate's African markets, where the chatbot functioned as the primary front line for student engagement and asynchronous tutoring sessions.
Governance focused on staged rollout across markets and operationalizing content curation and moderation workflows to maintain pedagogical accuracy and tone. Reported outcomes in cited coverage include increased conversational naturalness and scale for low data messaging channels and reaching millions of students with measurable improvements in student outcomes, reflecting the impact of Meta AI on Foondamate's tutoring and engagement functions.
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Mayo Clinic | Healthcare | 80221 | $17.9B | United States | Meta AI | Meta AI | Chatbots and Conversational AI | 2024 | n/a |
In 2024 Mayo Clinic implemented Meta AI to develop RadOnc-GPT, a radiation oncology large language model in the Chatbots and Conversational AI category used to support post-radiotherapy patient Q&A and clinician decision support. The project fine-tuned Meta's Llama 2 foundation model to create RadOnc-GPT and targeted initial deployments as patient follow-up chatbots to reduce clinician administrative burden within radiation oncology.
RadOnc-GPT capabilities implemented include a patient-facing follow-up chatbot for post-treatment questions and an internal clinician support assistant for decision support during follow-up workflows. The implementation centered on LLM fine-tuning, inference serving, and conversational prompt engineering consistent with Chatbots and Conversational AI functional patterns, with models hosted and tested inside Mayo Clinic operational environments.
Development and testing occurred within Mayo Clinic networks in the United States to ensure data security and to satisfy institutional review board oversight, indicating in-network model training and validation pipelines. Operational coverage focused on radiation oncology clinical workflows and patient follow-up processes, integrating the chatbot outputs into existing clinician workflows for question triage and administrative task reduction.
Governance emphasized IRB oversight and data security controls during development and rollout, with staged initial deployments limited to patient follow-up chatbot use cases to limit exposure. Mayo Clinic's deployment of Meta AI and RadOnc-GPT within the Chatbots and Conversational AI category reflects an institutional approach to embedding conversational AI into clinical operations while retaining in-network governance and oversight.
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Niantic | Media | 500 | $1.0B | United States | Meta AI | Meta AI | Chatbots and Conversational AI | 2023 | n/a |
In 2023 Niantic implemented Meta AI in its AR title Peridot to power conversational behaviours for in‑game pets called Dots, using the Chatbots and Conversational AI category to enable text and voice interactions and context-aware reactions in players real environments. The deployment targeted Niantic’s creative and entertainment area and was rolled out globally within the Peridot game, with the explicit aim of enabling more natural pet interactions for players worldwide.
The implementation centers on a conversational and agentic behavior module, driven by a customized Llama 2 model that Niantic hosts. Niantic configured the Meta AI integration to perform real-time inference for varied pet responses, supporting both text driven chat and voice input pipelines and aligning model outputs to in-game state and environmental context.
Integration work was focused on embedding Meta’s Llama models into Peridot’s runtime so the conversational module could access player context and AR sensor inputs to produce context-aware reactions in real environments. Operational coverage includes player-facing interaction systems and the game runtime layer, with model hosting and customization managed by Niantic to control behavior and latency characteristics.
Governance and rollout were executed as an in-game update, with Niantic retaining operational control of the customized Llama 2 model and conversational policies. The update explicitly improved interactivity and player engagement by generating real-time, varied pet responses, and module usage for conversational and agentic behavior is a stated component of the implementation.
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