“We Want to Become Israel’s Largest Data Company”
19 January, 2026
The merger of Israeli AI firms Tasq AI and BLEND aims to close the trust gap slowing enterprise AI adoption by combining data platforms, automation and human expertise
[Pictured, right to left: Erez Moskovitz and Yoav Ziv. Photo credit: Eyal Taug]
Two Israeli technology companies, Tasq AI and BLEND, have announced a merger into a single entity operating under the Tasq AI brand, in a move designed to tackle one of the central challenges of the enterprise AI revolution: the growing trust gap between the impressive capabilities of AI models and organizations’ willingness to deploy them in mission-critical operations. The merger brings together a data and AI quality management platform with a global network of human experts, aiming to create a trust layer that allows enterprises to train and operate models and AI agents with production-grade reliability.
The logic behind the combination is relatively straightforward. Over the years, Tasq AI has built infrastructure for managing AI data across the entire lifecycle, from model training to validating outputs in production. BLEND, which operated in the translation and localization space, brings deep experience with a model in which automation performs most of the work, while humans step into the loop to refine, verify and preserve meaning. Together, the companies are seeking to extend this approach far beyond translation into a broad range of enterprise AI use cases.
“Today, the real problem is no longer the AI models themselves, but the data and the trust around them,” says Yoav Ziv, CEO of BLEND, who will serve as CEO of the merged company, in a joint interview with Erez Moskovitz, founder and CEO of Tasq AI, who will take on the role of president. “Models can write, analyze and draw conclusions, but once an organization doesn’t trust the output, it simply won’t let them make real decisions.” In most scenarios where accuracy truly matters, he adds, a human still needs to remain in the loop, not to replace AI, but to enable it to operate safely.
This trust gap is especially evident in financial and regulated industries. An insurance company may deploy AI models to support underwriting decisions, and an airline may use AI to price tickets, but in both cases handing over full decision-making authority to an algorithm remains difficult. “A single mistake can cost a lot of money,” says Moskovitz. “To adopt models and agents in an enterprise, you need a process of refinement and adaptation to the organization’s data and domain. You don’t just take a foundation model and let it run.”
According to the two executives, the real challenge lies precisely in the transition from a general-purpose model to a specific organizational use case. Training, testing, fine-tuning and then testing again once the model is live in production are essential steps. This is where the merged company’s platform comes into play, enabling a layered combination of automation and different levels of human expertise. Tasks are broken down into small units, filtered automatically, reviewed by broad contributor pools, and only in complex or high-impact cases escalated to domain experts. This approach allows organizations to maintain accuracy and trust even when working with massive data volumes.
The translation world, where BLEND built its business, serves as a natural test case. For years it has been clear that machine translation can handle most of the workload, but without human review, meaning and context can easily be lost. “What happened in translation is now happening across all forms of information processing,” Ziv says. “This is a model that’s being generalized.”
Beyond the technology itself, both executives see the merger as a signal of a deeper shift in the future of work. “We’re moving toward a world where people don’t use AI, but AI uses people,” Moskovitz says. Experts don’t disappear, he argues, but enter the process at critical points, as part of a global network supporting enterprise models and AI agents.
Toward the end of the interview, Moskovitz articulates the company’s ambition in blunt terms. “We want to become Israel’s largest data company.” Behind the statement lies a belief that the next phase of the AI revolution will not be defined by yet another model or chip, but by the ability to refine data, build trust, and turn artificial intelligence into something enterprises can truly rely on at the core of their operations.
Posted in: AI
