--- format: "aidr-story-markdown/v1" id: "a2b9f170abad430ae6d55b5f6aa46d458002536c4d24c4a84e91204970675c8c" canonical_url: "https://aidr.today/a2b9f170?lang=en" title: "Creatify Launches Boreal-H3 for High Fidelity Video Ads" lang: "en" requested_lang: "en" available_langs: ["en","vi"] translation_fallback: null fallback_fields: [] published_at: "2026-09-30T18:23:19.000Z" category: "Releases" topics: ["agent"] source_urls: ["https://marketbrief.now/ai/creatify-launches-boreal-h3-for-high-fidelity-video-ads-549e9944","https://huggingnews.com/ai/creatify-launches-boreal-h3-for-high-fidelity-video-ads-549e9944"] summary: "An Ad Agent powered by Claude Opus 5.5 can now produce commercials in a single run using a specialized video generation model called Boreal-H3. Creatify developed the model by post-training MiniMax H3 on actual advertising projects, achieving 85.3% reference fidelity, which it identifies as the highest among evaluated frontier models. The system increased brief success rates from 28% to 50%, raised identity match from 83% to 94%, and reduced visible defects per clip by 70%, while cutting generation time and estimated costs by 20%. Boreal-H3 utilizes a closed loop system for recursive self-improvement rather than a standard one-off fine-tune. The process uses human-calibrated evaluation to diagnose failures, which then triggers targeted data collection, reinforcement learning, or inference optimization. All experimental results are stored in a shared memory that informs training decisions for subsequent model versions to maintain consistency in product shapes, labels, and dialogue." --- # Creatify Launches Boreal\-H3 for High Fidelity Video Ads > [Open the canonical story]() **Published:** 2026-09-30T18:23:19.000Z **Category:** Releases **Topics:** agent ## Summary An Ad Agent powered by Claude Opus 5\.5 can now produce commercials in a single run using a specialized video generation model called Boreal\-H3\. Creatify developed the model by post\-training MiniMax H3 on actual advertising projects, achieving 85\.3% reference fidelity, which it identifies as the highest among evaluated frontier models\. The system increased brief success rates from 28% to 50%, raised identity match from 83% to 94%, and reduced visible defects per clip by 70%, while cutting generation time and estimated costs by 20%\. Boreal\-H3 utilizes a closed loop system for recursive self\-improvement rather than a standard one\-off fine\-tune\. The process uses human\-calibrated evaluation to diagnose failures, which then triggers targeted data collection, reinforcement learning, or inference optimization\. All experimental results are stored in a shared memory that informs training decisions for subsequent model versions to maintain consistency in product shapes, labels, and dialogue\. ## Sources - [Story source]() - [Story source]()