BLEND has announced a strategic merger with Tasq.ai, a leader in high-scale AI model training, marking a pivotal milestone in its evolution toward closing the enterprise AI trust gap and broadening its service portfolio beyond localization. The newly unified entity will focus on building a robust “Trust Layer” for global AI deployments by combining Tasq.ai’s proprietary Data Refinery technology with BLEND’s curated network of 25,000 domain experts, enabling a platform where AI is trusted because humans leverage their edge. This merger allows BLEND to extend its long-standing mission of seamlessly blending AI with expert human insight into the critical data foundation underpinning enterprise AI systems, ensuring higher quality and trust in AI outputs. Despite this expansion, BLEND’s commitment to hassle-free localization remains steadfast with the same dedicated teams and points of contact for clients, now enhanced with human-in-the-loop validation and data annotation services that help ensure AI initiatives are accurate and safe. The integration positions the combined company to better serve some of the world’s most recognizable brands by scaling AI adoption with the quality and trust clients expect, while maintaining and improving its core localization offerings.
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This strategic move underscores BLEND’s vision to not only support enterprise AI adoption through superior localization solutions but also to fortify the underlying data processes that drive trusted AI outcomes. By embedding Tasq.ai’s capabilities into its ecosystem, BLEND aims to address a persistent industry challenge ensuring reliable, high-quality data that underpins predictive models and AI systems at scale. Clients of BLEND will enjoy improvements in data annotation and validation in conjunction with the translation and localization offering, further solidifying the position of BLEND as a ‘one-stop partner for all AI and language needs globally.’ Through the integration of the companies, the move is set to improve the adoption of AI across numerous enterprise applications without shaking client confidence.
