News and updates from the Wikimedia Enterprise team
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Join Wikimedia Enterprise at the February seminar of Stanford’s Institute for Human-Centered Artificial Intelligence! The Stanford HAI Institute offers a front-row seat to some of the most pressing questions in AI today, covering how AI systems are built and how they interact with society at scale. The February HAI seminar will feature Wikimedia Enterprise representatives…
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Mistral AI and Wikimedia Enterprise are excited to announce the start of a three-year partnership. Data from Wikipedia and other Wikimedia projects is indispensable as a source of knowledge for Mistral’s AI projects, such as its AI assistant Le Chat. This partnership enables them to reuse free Wikimedia content via Enterprise APIs to meet their…
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Wikipedia celebrates 25 years of human-created knowledge on 15 January 2026. To mark this milestone, Wikimedia Enterprise is publicly announcing partnerships with Amazon, Meta, Microsoft, Mistral AI, and Perplexity. Learn how our infrastructure delivers human-governed knowledge at scale.
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Read our full annual wrap-up to explore how 2025 marked a fundamental shift in open knowledge. From Wikipedia’s recognition as a Digital Public Good to the launch of Parsed Tables and Quality Scoring models, discover how we are helping developers build a sustainable future for ethical data with Wikimedia Enterprise.
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Reef Media is building a comprehensive platform to help users analyze media for its strengths and weaknesses, creating a more informed public. One of the key datasets underpinning their technical strategy is a robust, verifiable data source: Wikimedia data extracted through the Wikimedia Enterprise Snapshot API.
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Learn about the intersection between generative AI data and open, trusted datasets in the talks by Wikimedia Enterprise, MLCommons, and the AI Alliance at NeurIPS.
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Access Wikipedia’s most valuable tables as structured JSON with the new Parsed Tables feature from Wikimedia Enterprise. Instantly convert complex tables into clean, machine-readable data without scraping. Enhance your AI, search, and knowledge graph projects with reliable, human-curated facts that were previously locked away in HTML and wikitext.
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Nomic AI used Wikimedia Enterprise’s Structured Contents dataset, via Hugging Face, to build the first full open-source vectorization of multilingual Wikipedia. Their work highlights how structured open data can accelerate AI research, improve model performance, and enable new forms of data visualization.
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Explore Wikipedia content in a clean, structured format with our new beta dataset on Kaggle. Built from our Snapshot API using the Structured Contents beta, it’s ideal for data science, ML training, and experimentation.
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Wikimedia Enterprise is partnering with ProRata.ai to power its new search engine, Gist.ai, with reliable, human-curated Wikimedia content. The collaboration supports a sustainable content ecosystem through transparent attribution and API-driven innovation—ensuring creators are credited and content remains discoverable in the AI era.
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The latest API release boosts Wikipedia data integration with parsed references in JSON and two quality scoring models – Reference Need and Reference Risk. These enhancements streamline citation access and improve content reliability for developers.
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Wikimedia Enterprise joined Creative Commons at SXSW 2025 on March 9th in downtown Austin for a day of insightful conversations and panels exploring the intersection of artificial intelligence and open data. The discussions emphasized the critical importance of protecting and ethically advancing open access principles amidst rapid technological growth. The event brought together industry leaders,
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Wikimedia Enterprise and Pleias are partnering to drive ethical AI innovation with high-quality, structured data. By integrating Wikimedia’s verifiable datasets, Pleias enhances its AI models while ensuring openness, auditability, and multilingual accuracy.
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A Partnership for Sustainability of Knowledge and the Planet – Wikimedia Enterprise has announced an exciting new partnership with Ecosia search engine.
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Discover how Wikimedia Enterprise APIs transformed in 2024 with parsed article sections, an AI focus, and greater free access. Learn about our enhanced developer tools and how to leverage Wikipedia’s dynamic content with recurring API access and improved data structures.
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We’re releasing an early beta dataset on Hugging Face, offering structured content from English and French Wikipedia. This machine-readable dataset, derived from our Snapshot API’s new Structured Contents beta, opens up new possibilities for AI and machine learning applications.
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Snapshot API now includes a beta Structured Contents endpoint, offering bulk access to parsed Wikipedia data for testing partners.
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We’re excited to announce a major upgrade to our free API tier. Users now benefit from recurring monthly credits, replacing the previous lifetime limit, and enjoy twice the speed in data updates. These enhancements are designed to provide more value and flexibility to our free account holders.
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Unlock deeper insights into Wikipedia content with Wikimedia Enterprise’s Credibility Signals. Learn how these tools provide critical context, empowering informed decisions for AI models, knowledge graphs, and beyond.
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Discover how to leverage the Wikimedia Enterprise On-demand API to improve the contextual accuracy of open-source language models like Meta’s Llama 3. Our tutorial walks you through setting up a local RAG-based application for more accurate AI responses.
