ZETTAI Inc. Launches 'Claude Code Training e-Learning Version', Enabling Non-Engineers to Implement AI in 10 Days

Reflecting all the stumbling blocks accumulated from in-person CLAUDE training into the system.

We have prepared a highly reproducible curriculum that even high school students can complete.

ZETTAI Inc. (Headquarters: Minato-ku, Tokyo; CEO: Kazuto Ogata) launched the "Claude Code Training e-Learning version" on May 20, 2026, to support corporate AI internalization.

This service is a corporate training program that allows employees with no programming experience to plan, prototype, and publish AI-utilized operational improvement tools in a short period.

To address corporate challenges such as "We introduced AI, but it is not being utilized on-site" and "We have ideas for improvement, but no talent to implement them," we support the creation of a structure where prototyping and improvements can first be done internally before outsourcing.

Book a 30-minute free briefing session

Solving the common corporate dilemma: AI adoption ending merely as a "topic of conversation."

Even when many companies introduce generative AI, only a limited number of employees can actually apply it to operational improvements. As a result, improvement ideas stall on-site, even minor operational tweaks require outsourcing, initiatives end at the Proof of Concept (PoC) stage without progressing to field implementation, and AI adoption simply ends up generating buzz.

This training is provided to address these issues by elevating employees to a level where they can independently shape operational improvement tools, thereby accelerating the company's speed of improvement.

It is a learning system emphasizing reproducibility that has achieved a 99% e-learning completion rate by systematizing the stumbling blocks accumulated from in-person corporate training where 88% of participants were non-engineers.

Service details and implementation consultation: https://claudecode.co.jp/

■ Why we need companies that can "improve with AI" rather than just "use AI" right now

The adoption of generative AI itself is rapidly advancing.

However, in many companies, its utilization is limited to "text creation" and "information retrieval," failing to step into the actual improvement of business operations.

What is truly important is creating a state where operational staff, understanding their own tasks, can rapidly iterate small improvements utilizing AI. There is massive room for improvement in areas like sales, HR, administrative departments, and customer support.

Yet, because there is no talent who can "implement" these solutions, many improvement ideas remain stalled.

This training service is provided with the goal of nurturing not just "talent who can use AI," but "talent who can improve internal operations using AI."

■ Training strictly focused on a structure that reaches field implementation in the shortest time

In this service, learners study only the strictly necessary elements—such as Claude Code, Render, and Supabase—to achieve field implementation in the shortest possible time.

Based on over 300 days of experience using CLAUDECODE since its release,

From a non-engineer's perspective,

We have designed the curriculum so that fundamental literacy can be learned with minimal effort, based on experiential insights of "this part is unnecessary" and "learning just this part is sufficient."

Through 39 hands-on stages, learners experience a seamless process: organizing operational issues, designing instructions for AI, prototyping tools, managing data, and publishing to production.

Rather than mere acquisition of AI knowledge, the structure allows learners to acquire practical experience in the shortest time by calculating backward from the goal and learning only what is necessary.

Consistently learning from organizing operational issues to production release across all 39 stages.

Furthermore, this service incorporates the following into the curriculum design from the start:

"What to build first"

"Where people are likely to stumble"

"What order to proceed in so progress doesn't stall"

Therefore, rather than simply learning how to operate tools, a key feature is that learners can easily begin prototyping operational improvements from the very day they learn them.

■ Aiming not for "AI education that ends upon course completion," but a state where "field improvements begin to iterate."

What this service prioritizes is not that the learner simply ends up "understanding AI."

We place importance on learners building tools close to actual operations by themselves after the training and starting to use them internally.

■ Changes Before and After Implementation

Before: Improvement ideas stall in meetings -> After: Small prototypes can be built on-site

Before: Even minor modifications require outsourcing -> After: Initial prototyping can be done internally

Before: AI utilization relies on a few employees -> After: Improvement themes emerge from various departments

Before: Vague requirement definitions -> After: Hypotheses can be tested internally

Before: Initiatives stop at PoC -> After: Easier progression to field operation

The important thing is not to build large-scale systems from the start, but to create a state where teams can improve small inefficiencies on-site by themselves.

Once the first tool can be shaped internally, subsequent improvement themes become easier to iterate within the company, fostering a culture of trying things internally first before outsourcing.