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What we did<\/h2>\n
We have designed and deployed a multimodal moderation platform<\/strong>, capable of automated analysis of both images and text before they are published. Thanks to its approach based on advanced artificial intelligence models, the system can detect problematic content with a high level of accuracy, operating in real time or through batch processing.<\/p>\n Notable detection capabilities include:<\/p>\n In addition, the solution is highly flexible and can be integrated into multiple digital channels<\/strong>, including e-commerce platforms, community forums, social networks, chatbots, suggestion boxes, review systems and more.<\/p>\n \u00a0<\/span><\/p>\n We have already successfully applied this technology in different sectors, demonstrating its scalability and effectiveness:<\/p>\n \ud83d\udecd\ufe0f International retail<\/strong>: we perform automated analysis of more than 30,000 images every week<\/strong> in digital catalogs, marketplaces and advertising campaigns, with the aim of avoiding the publication of offensive, misinterpretable or culturally inappropriate elements.<\/p>\n \ud83c\udf0d NGOs and social platforms<\/strong>: we help moderate user-generated content (UGC) in open digital communities, ensuring that virtual spaces are respectful, inclusive and safe for all participants.<\/p>\n These cases demonstrate that, beyond the industry, there is a growing demand for technology solutions that prevent reputational crises and foster healthy digital environments.<\/p>\n <\/p>\n Our solution combines different artificial intelligence technologies, computer vision, natural language processing (NLP) and cloud services. The system is composed of the following key components:<\/p>\n BLIP<\/strong>: language-vision model that generates automatic image descriptions and answers questions about visual content.<\/p>\n<\/li>\n YOLO<\/strong>: neural network specialized in object detection and localization, used to identify offensive or dangerous elements.<\/p>\n<\/li>\n MobileNet<\/strong>: lightweight architecture optimized to recognize complex cultural patterns such as traditional textiles, religious iconography or local symbology.<\/p>\n<\/li>\n LLaMa 3.2 Instruct<\/strong>: PLN model oriented to classify texts with high sensitivity to hate speech, harassment, stigmatization or misinformation.<\/p>\n<\/li>\n Claude Sonnet<\/strong>: multimodal model with high accuracy in moderating mixed content (image + text), useful for analyzing integrated publications.<\/p>\n<\/li>\n External APIs<\/strong>: such as Azure Content Moderator and Google Cloud Vision, integrated as additional reinforcement.<\/p>\n<\/li>\n Data sources<\/strong>: we use public datasets (Roboflow, Kaggle), web scraping (Wikimedia, Unsplash) and synthetic image generation with Stable Diffusion under commercial license.<\/p>\n<\/li>\n<\/ul>\n \u00a0<\/span><\/p>\n The system has proven its effectiveness and value in real environments:<\/p>\n \u2705 Automated detection of 31 categories of offensive symbols<\/strong> and inappropriate elements. \u00a0<\/span><\/p>\n A misinterpreted image or a misplaced message can trigger an immediate reputational crisis<\/strong>, generate viral reactions, cause cancellations, boycotts or even legal sanctions. In the case of NGOs or social platforms, the risks are even more sensitive, as they can affect the trust, emotional security and well-being of users.<\/p>\n Our solution allows you to act in advance, minimizing risks and aligning the content published with the values, norms and ethical standards of each organization. It is a system that not only filters out problematic content, but also allows you to communicate with greater responsibility, sensitivity and cultural coherence.<\/strong><\/p>\n At hiberus, we firmly believe that technology should be at the service of respect, inclusion and integrity.<\/strong> With this content moderation platform, we demonstrate that it is possible to scale digital processes without losing the human context<\/strong>, helping brands and communities to protect their image, build safer environments and strengthen the trust of their audiences.<\/p>\n\n
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<\/span><\/p>\nUse cases<\/h2>\n
How it works<\/h2>\n
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Results achieved<\/h2>\n
\u2699\ufe0f Batch processing of thousands of images in seconds<\/strong>, enabling frictionless scaling of content review.
\ud83d\udd0d Significantly improved reputational protection<\/strong> as well as digital environment safety<\/strong> for brands and communities.<\/p>\nWhy it matters<\/h2>\n
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