日本語 
ai& Inc. has entered into a partnership with Ryosan Ryoyo Co., Ltd. to expand the deployment of AI inference solutions across Japan’s enterprise market.
The partnership combines ai&’s AI inference technologies with Ryosan Ryoyo’s strengths in hardware procurement, infrastructure configuration, and enterprise sales. The goal is pretty clear here. Help Japanese companies get advanced AI models up and running in real business settings, without getting stuck in all that infrastructure stuff, setup headaches, and those deployment delays that really slow everything down.
Generative AI adoption is moving fast across customer support, operational efficiency, research and development, and even software development. But the bigger issue for many enterprises is not experimenting with AI anymore. It is building an inference environment that is stable, secure, scalable, and financially predictable enough to run AI inside actual business operations.
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That becomes even more critical for organizations handling sensitive or confidential data. Performance alone is not enough. Companies also need stronger control over data handling, security, operational management, and long-term infrastructure costs.
This is where the partnership is positioning itself.
As one of ai&’s hardware partners, Ryosan Ryoyo will support procurement and configuration of GPU-accelerated server infrastructure using its relationships with major hardware vendors. The company will help deliver hardware setups optimized specifically for ai&’s inference solutions and workload requirements.
At the same time, Ryosan Ryoyo will also act as an enterprise sales partner for ai&’s solutions in Japan, supporting proposals, deployment, and implementation for domestic companies looking to integrate generative AI and AI agents into their operations.
The move also targets a problem that keeps slowing enterprise AI projects down in Japan. Building and operating GPU infrastructure often involves multiple vendors, system integrators, procurement layers, and maintenance providers. That usually means higher costs, longer deployment cycles, and operational headaches.
By combining infrastructure support with AI inference capabilities under one partnership structure, both companies are aiming to simplify implementation while reducing deployment time and operational burden for enterprises adopting AI at scale.
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