This article is the entry for day 7 in the KINTO Technologies Advent Calendar 2024 🎅🎄

Introduction

Hello! I'm Iki, from the Cloud Infrastructure Group at the Osaka Tech Lab. In this article, I’ll share how the Osaka Tech Lab team applied the MVP development method to build a chat API powered by generative AI.

What is MVP (Minimum Viable Product) Development?

This method consists of first creating a product that has the bare minimum of features, then verifying and improving it while getting feedback from users, in this case, the Product Manager (PdM) who originally suggested creating it. I think it was an extremely efficient development method for handling the rough requirements we had for this project.

What Inspired Us to Create a Chat AI

The idea came about naturally.

KINTO Technologies has offices in Tokyo, Nagoya, and Osaka (Osaka Tech Lab), but nearly 80% to 90% of its employees are based in Tokyo. Since work isn't assigned by location, everyone, regardless of their office, contributes to Tokyo-led projects.

This setup isn't an issue at all —if anything, Osaka team members are particularly close-knit compared to those at other locations! (At least, in my opinion.)

Still, at some point, the Osaka members started thinking wouldn’t it be great to work on something uniquely Osaka-based? Around that time, they happened to cross paths with a Product Manager who was exploring the potential of chat systems powered by generative AI.

Theme

For this project, we decided to use MVP development to verify whether chat using generative AI was a viable idea.

However, the term “chat” covers a wide range of situations, with customer service and reporting to bosses also being examples. Rather than tense, stiff conversations, we focused on whether it could emulate the kind of natural, relaxed way that people chat with family and friends.

What We’ve Managed to Create So Far

Overall structure Note: Deciding that it would be better if there was a robot in the chat, we used BOCCO emo, a commercial robot by Yukai Engineering Inc.

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What We Considered When Creating the Chat AI

Time

The goal this time was to explore whether conversations powered by generative AI could work. However, even if we succeeded by investing a lot of time and effort, generative AI-powered conversations already exist in the world, so achieving that is a given.

In addition, the theme centers on conversation, a natural and intuitive part of human interaction. However, we soon realized that we had chosen an extremely insightful theme, sparking a wealth of ideas for what we aspire to create. Consequently, there would be no end to what we could do if we had the time.

Thinking that it wasn't worth spending too much time to create an MVP, we proceeded with the creation with an aim of completing it within the timeframe we had set.

How much time we spent on MVP creation

• Considering the requirements and creating the MVP
  • 2 days
• Verifying and improving it while getting feedback
  • 15 hours (max)

What Did We Actually Do?

Considering the requirements and creating the MVP

At the beginning, we didn't selected an environment to develop the MVP, while lacking any knowledge of the way to create a generative AI-powered system. With things as they were, before we got as far as verifying the theme (i.e., whether natural, carefree chat can be achieved), we would have first had to start from verifying how to create a generative AI system. However, for expertise about generative AI systems, we got help from ZEN ARCHITECTS, the company that provides the Azure Light-up program. This created a situation that would enable us to focus on the theme.

Besides accompanying us along the way as we were building the generative AI system, ZEN ARCHITECTS also gave us ideas based on actual experience (for example, things we should be careful about when using generative AI with a theme as rough as ours), and pulled us along so that we managed to complete the MVP in 2 days.

Verifying and improving it while getting feedback

Based on comments from actually trying it out, the development members discussed and decided on what improvements to make. We added a feature to let you chat from your current location, and in order to fix issues with robots that only ever chatted in cafes, we spent a month (15 hours) running a cycle of getting feedback on things people noticed about the prompts and so on.

To verify the feature for chatting from your current location, we did not simply do it all from the office by changing our location data, but actually went to different places physically. Since KINTO is a car subscription provider, we also did fittingly unique real-time updates, such as ones deployed while receiving feedback in a car.

Creating it in-house means we can do things like this! (We repeatedly ran verifications and made improvements with this attitude to guide us!)

Conclusion

The chat API using generative AI that we created in this project is currently undergoing in-house verification regarding its future potential. If its value exceeds how much it is expected to cost in the future, we plan to push ahead with developing it further.

That said, continued development might get canceled if it is deemed to be premature. However, even if it does not get continued, the things we confirmed and experience in a new area (Azure development of generative AI systems) we gained will still remain as outcomes of the project.

Those outcomes will fuel new ideas. (For example, they can be fed back into existing systems.) Even if this project does not get continued, I will not think of as a failure. I think we can create an environment that will enable us to constantly move forward and run the innovation cycle, in order to do MVP development that will not fail.

I would like to push forward with tackling the challenges that arise with MVPs as an end unto itself!

Also, if you want to know a little more about the details, ZEN ARCHITECTS have published a case study on the project, so please check that out.

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