We are not afraid of leveraging our theory to accomplish your business objectives, or even further; to dive into academia if necessary.

If you have ever encountered a difficult situation, not exclusively, but remarkably when making decisions with regard to business management, that is one of the instances where our support will make large difference.

A lot of insights come from data, so you may be surprised when uncertain factors that obstruct your judgement are vastly reduced.

At the first place, we are IT professionals responsible for creating an overall picture of the future ‘business’, in terms of a system employment.

Although it’s not necessarily true that we must use a computer system to start a business, it becomes greater the amount of well-handling and the extent of manageability if one chooses to do so over time.

For a decade, we have been developing quite a few core systems, from a tiny monolithic singleton service to a large complex system that admits modern architecture of integrated smaller services.

Equally important to the business strategies and technical domain knowledge accumulated there, we recognized “evaluability”.

PDCA, which is said to originate from the “Shewhart Cycle” proposed by Dr. Walter Shewhart, a pioneer of statistical quality control in the early 20th century, still plays a fundamental role in applied contexts such as quality control and business improvement.

Even in the manufacturing industry, where quality control is extremely important due to industry characteristics, we have often seen cases where, due to organizational structure, emphasis is placed on “visual inspection” by experienced and influential individuals, often lacking a clear definition, validity, or verifiability of the evaluation itself.

It is thought that erroneous operation of the “C” (Check = Evaluation) in PDCA — and consequently the “A” (Act) based on it and the subsequent recurring “P” (Plan) — occurs in this way, resulting in a lack of improvement over long periods.

Ultimately, the essence of evaluation lies in a sense of satisfaction or consensus, and involves heuristic judgment.

As experts, we respect mathematical quantitative evaluation above all else, though precisely because of this, we place importance on the fact that not all stakeholders will necessarily be convinced by it.

This is one of the most important values we can provide.

Today, the scope of data science is getting wider, and in addition to traditional analysis — evaluation, and mining — the demand for inference (prediction), modeling, and visualization seems to be increasing.

Furthermore, the systems that support these, and their architecture, must be able to withstand a wide range of requirements such as scalability, reliability, security, and their measurability; in which many of the rooted concepts can be found in the SLOs’ context. To consider and properly investigate the conditions for which the demands come arise, the solutions would almost never be closed in a computer system; similarly, among such, every concerns and problems arise in a business are not necessarily, or at least not directly computer integrated.

We confidently attest that one who enlisted our service, maybe as the solution for vague questionaries, such as “What would be a set of candidates for which the gained profit would be maximized?” or “What is a best reasonable steps to go through when one begins a business of specific domain?”, will experience a promising change in the deep level of perspective.

On the other hands, one who are determined, considering the use of existing system, may want to start out more practical approach. In that case, we offer the definitions of requirements after elaborate listening and observation of our clients.

The definition of requirements is essential to select models or frames to which we refer for suggesting an appropriate plan, since it extracts the minimum requirements of what you want to achieve.

Please note that there is no “golden plan” that is optimal for every possible business or system; rather we are to advise you to eliminate overly broaden and uncertain requirements that results costly cumbersome architecture of a system.