Search “Multimodal”

Yixin Huachen Releases Ruizhi OPT Multimodal Data Governance Platform

On October 30, 2026, Yixin Huachen released the new-generation Ruizhi OPT multimodal data governance platform. The platform adopts an AI-native architecture, with more than 30 built-in data governance expert agents, over 50 governance skills and more than 100 knowledge bases, and encapsulates governance capabilities such as metadata, data standards and data quality as a CLI. The platform uses a four-layer architecture and provides three working modes: fully automatic execution, human-machine collaboration and manual revision, with multimodal governance covering six stages from collection and aggregation to scenario-based delivery. At the 20th anniversary customer conference, Yixin Huachen General Manager Mao Daqun proposed the viewpoint of the "double helix of AI and data" and released the Ruizhi OPT agent working architecture, saying that under this architecture, governance work that originally required a team half a year to a year can be completed by one person.

Yixin Huachen releases Ruizhi OPT multimodal data governance platform

On October 30, 2026, Yixin Huachen released the new-generation Ruizhi OPT multimodal data governance platform. The platform adopts an AI-native architecture, with more than 30 built-in data governance expert agents, over 50 governance skills and more than 100 knowledge bases, and encapsulates governance capabilities such as metadata, data standards and data quality as a CLI. The platform uses a four-layer architecture and provides three working modes: fully automatic execution, human-machine collaboration and manual revision, with multimodal governance covering six stages from collection and aggregation to scenario-based delivery. At the 20th anniversary customer conference, Yixin Huachen General Manager Mao Daqun proposed the viewpoint of the "double helix of AI and data" and released the Ruizhi OPT agent working architecture, saying that under this architecture, governance work that originally required a team half a year to a year can be completed by one person.

View story:Yixin Huachen Releases Ruizhi OPT Multimodal Data Governance Platform

Rohan Paul said that Sunra resells multimodal model calls at the manufacturer's original price

Rohan Paul stated on October 29, 2026 that Sunra has released the Sunra API, through which users can call all major image, video, audio, and 3D models with a single key, billed at each model vendor's own API prices on a pay-as-you-go basis, with no subscription fee and no markup. Rohan Paul also said that Seedance 2.5 is priced on Sunra at half of what two other gateways charge, and that this unified entry point also makes it easier to run A/B tests on models from Google, OpenAI, ByteDance, and Kling. At present, this claim appears only in Rohan Paul's account, and there is no independent evidence from Sunra officially or from other sources.

Rohan Paul says Sunra resells multimodal model calls at the vendors' original prices

Rohan Paul stated on October 29, 2026 that Sunra has released the Sunra API, through which users can call all major image, video, audio, and 3D models with a single key, billed at each model vendor's own API prices on a pay-as-you-go basis, with no subscription fee and no markup. Rohan Paul also said that Seedance 2.5 is priced on Sunra at half of what two other gateways charge, and that this unified entry point also makes it easier to run A/B tests on models from Google, OpenAI, ByteDance, and Kling. At present, this claim appears only in Rohan Paul's account, and there is no independent evidence from Sunra officially or from other sources.

View story:Rohan Paul said that Sunra resells multimodal model calls at the manufacturer's original price

Miles summarizes the eight-step multimodal data cleaning solution for FDE at beauty companies

On October 28, 2026, Miles summarized an eight-step multimodal data cleaning solution for FDE implementation projects at beauty companies. The solution processes text, image, audio, and video data collected from Xiaohongshu, Douyin, WeChat Channels, and Weibo through eight steps: defining data specifications, preserving original content, mapping data relationships, multimodal normalization, conflict and quality cleaning, generating structured data, performing acceptance checks, and storing data by rules and versions. The original text emphasizes that cleaning results must not overwrite the original text, and that OCR, ASR, and model labels serve only as newly added results; when ASR and OCR values conflict, the original evidence is traced back, and if it cannot be confirmed, the item enters a pending-verification queue. This pipeline has also been organized into a general solution that can be reused in scenarios such as consumer goods insights, automotive word-of-mouth, restaurant site selection, and brand public opinion.