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An AI agent uses generative AI to support tasks such as gathering information, making judgments, writing, and operating systems. Rather than just answering questions, it can carry out the work needed along a business flow and assist people with their work. Its defining feature is that AI can support the checking, organizing, and drafting people used to do by hand, right within your business processes.

ChatGPT and typical chatbots are mainly designed to answer user questions. An AI agent, on the other hand, can refer to the information it needs, make judgments, and move work forward while working with external tools and internal systems. It helps to think of it as an evolution from “AI that answers” to “AI that gets work done”.

It can be used for many kinds of work, including handling inquiries, searching internal knowledge, summarizing meetings, writing reports, supporting sales, checking applications, drafting emails, and entering data. It is especially well suited to work that involves gathering and organizing information, making certain judgments, and putting the result into writing. A major strength is that it can handle not only routine work but also work that requires judgment and understanding of context.

Yes. Through API and data integration, it can connect with your existing internal systems and various SaaS products, for example by referring to internal databases or working with workflow and customer management systems. We can design AI agents to fit into your existing business environment.

Yes. We can build AI agents that work with business tools such as Microsoft 365, Google Workspace, Slack, and Teams. The agent can use email, calendars, chat, and files, so you can hand off everyday tasks to it or have it assist you. A big advantage is that AI agents fit naturally into the tools you already use every day.

We draw the line based on the importance, risk, and impact of the work. Gathering information, drafting, and first-pass checks are easy to leave to AI, while external communications, contract decisions, payment approvals, and important decisions are usually designed to be checked by people. It’s important not to leave everything to AI, but to keep checkpoints where people review the work..

Yes. We reduce the risk of wrong answers and errors by combining clear sources, human approval steps, permission management, logging, prohibited operations, and evaluation with test data. With a solid understanding of how AI behaves, we design safety measures to fit your work. Rather than using AI output as is, it is important to build in mechanisms to check, control, and record it.

The main points are handling confidential information, access rights, data sent to external services, log management, usage rules, and the impact of mistakes. AI agents are convenient, but they may access business systems and internal data, so design before implementation is important. We recommend clarifying your security requirements and starting with the areas where it can be used safely.

Yes. With proper permission and data management, we can build AI agents that make use of your internal data. When handling highly confidential information, however, the AI platform, data storage, access control, and log management must be designed carefully. When handling confidential information, it is important to design not only the technology but also the operating rules.

First, you need to organize the target work, clarify the purpose, review the data and documents it will refer to, and check whether it can connect to existing systems. Rather than rolling it out at scale right away, it is easier to start small with work where the benefits are easy to see. The first step to success is organizing the work and clearly defining what to leave to AI.

It depends on the work and the scope of integration, but a simple PoC takes a few weeks, and full production a few months. The timeline varies with business requirements, security requirements, and whether existing systems need to be connected. A realistic approach is to validate on a small scale first, then move to production step by step while confirming the results.

Yes. You can try it in a limited scope, such as inquiries for a specific department, internal document search, or routine reports. Starting small lets you check the benefits and issues and expand at a comfortable pace. Starting with limited work rather than a company-wide rollout reduces the risk of implementation.

We recommend starting with departments that handle many inquiries, review many documents, or do a lot of routine work, such as sales, customer support, HR, accounting, legal, IT, and corporate planning. AI agents tend to deliver the most benefit in high-volume work with fairly well-defined criteria for judgment.

We evaluate it based on answer accuracy, processing success rate, validity of sources, time saved, user satisfaction, error rate, and more. Setting evaluation criteria at the PoC stage also makes it easier to decide on production. It is important to evaluate not just technical accuracy, but whether it actually helps in real work.

Based on usage logs and feedback from the front line, we continuously improve prompts, reference data, workflows, permission settings, and more. An AI agent is not something you build once and leave; it needs to be adjusted to how it is actually used. By nurturing it after implementation, you can evolve it into an AI agent that fits your work even better.

Yes. We can build a dedicated AI agent tailored to your work, internal rules, systems, and data structures. It can also handle company-specific processes that general-purpose AI tools struggle with. Our strength is that by designing around your work, we can build an AI agent that is easy to use in practice.

Yes. You can combine agents that gather information, check content, write text, request approvals, and so on. Dividing roles makes it easier to handle more complex workflows. By linking multiple agents, you can support entire business processes, not just single tasks.

We estimate it based on the time spent on the work, the number of cases handled, error rates, review workload, outsourcing costs, and more. We assess not only labor cost savings but also faster response, more consistent quality, and less dependence on specific individuals. It is important to look at cost-effectiveness including improvements in quality and speed, not just reduced workload.

Yes. We offer support to help it take root after implementation, including hands-on training for users, operations training for administrators, prompt writing training, and setting up rules for AI use. We focus not just on introducing the tool, but on supporting you until your teams can use it with confidence.

Yes. To use AI agents effectively, it is important not only to automate existing work as is, but also to rethink processes and roles. We can help with organizing work, proposing improvements, and making it stick after implementation. Introducing AI can be the opportunity to reshape the work itself into a more efficient form.