Category
Articles about AI and Machine Learning
On October 29, 2026, at an internal corporate study session led by Representative Kousuke Terada, we tested two techniques on a small language model (Qwen2.5-3B-Instruct): "providing examples" and "asking for a review." Based on questions that improved, worsened, or confidently asserted an error was correct, we explain how to evaluate generative AI for business operations.
Before using a local inference API inside the company, design authentication, an allow list and device retirement as separate things. An allow list shows which peer may be reached. Who may use it for which work is decided separately.
Field AI that runs near cameras and sensors, and a local LLM that handles internal documents, care about different conditions. Cor.Inc.'s policy is to separate the verification environments. Separation itself is not proof of safety.
For executives and administrative departments unsure whether they can input contracts, meeting minutes, estimates, or customer information into generative AI, this article explains how to classify confidentiality levels, choose between cloud AI and local LLMs, and establish internal rules.
Given the current 20.4% AI adoption rate among SMEs, this article explains how to start utilizing AI without overextension, beginning with general affairs and administrative departments. Written for companies considering their first steps in AI utilization.
For executives and administrative departments formulating rules for generative AI use, this article outlines prohibited input information, approved AI tools, logging, education, and incident response. It is intended for companies seeking to balance AI utilization with information management.
We explain why internal document search and RAG implementations fail to deliver results, looking from the perspectives of data organization, permissions, evaluation metrics, and operational design. This is a practical guide to keep internal knowledge AI from ending at the PoC stage.
We explain the basics of local LLMs, how they differ from cloud AI, the types of operations best suited for adoption, and the security and operational requirements to verify before a PoC. This article is for companies looking to securely leverage AI with confidential data.
We explain why AI adoption stalls at the PoC stage, along with the KPIs, verification design, and internal decision-making processes needed to drive business results. An article for business executives and PMs aiming for full-scale production deployment of AI.
We explain how to organize the purpose, KPIs, costs, risks, and estimate rationales required to get internal approval for AI adoption. This article is for professionals looking to explain AI implementation within their company.
In 2026, as the adoption of AI agents advances, this article explains the permission design, operation logs, and human approval points that should be determined before integrating them into operations. This implementation policy ensures projects do not end at the PoC stage.
GitHub contribution counts were about 20–40 a month in the second half of 2024, and about 300 in May 2025. The count is GitHub's standard total, including issues, pull requests and reviews, based on the graph posted on 30 May 2025. It is not a rate of development speed or delivery volume.
