This post is for Day 2 of Merpay & Mercoin Tech Openness Month 2025, brought to you by @ben.hsieh from the Merpay Growth Platform Frontend Team.
Merpay Growth Platform develops an internal platform for Mercari’s user engagement and CRM activities, empowering marketing users.
This article introduces our efforts to evolve our internal platform driven by AI.
Background
For approximately four years, the Merpay Growth Platform has developed an internal platform called Engagement Platform. Previously, Mercari had disparate tools and services addressing similar problems independently for various use cases, leading to redundancy.
To address fragmented processes and diverse use cases, the Engagement Platform was developed as a unified solution. This necessitates close collaboration with marketing teams to understand their specific needs and deliver a flexible solution capable of handling a wide variety of applications.
The Role of Frontend Team
Building internal systems might seem easier because they have fewer users. However, the Growth Platform Frontend Team has been quite ambitious over the past few years, developing our internal platform into a full-fledged CMS and CRM admin dashboard.
This means it’s a full-stack operation, requiring us to address both the UI/UX of the admin tools and the challenges of the content service to handle Mercari’s extensive user activity in the production environment. To know more about this team’s interesting initiatives, check our posts previous below:
- WYSIWYGウェブページビルダーを支える技術とSever Driven UIへの拡張
- Enhancing Collaboration and Reliability: The Journey of Version History in our Page Editor Tool
- 【書き起こし】WYSIWYGウェブページビルダーを支える技術的マジックの裏側 – Hal Amano / Arvin Huang / Ben Hsieh / Jas Chen【Merpay & Mercoin Tech Fest 2023】
Significance & Challenges of Admin System UX
Internal tools often get the short end of the stick when it comes to good design. But our team is determined to change that. We’re aiming to build an internal platform with a really polished, user-friendly feel – like something you’d see in a real product.
That means tackling the tricky bits of both our admin tools and content systems, so our marketing folks have a smooth experience even with tons of user activity. The ultimate goal is to help empower non-engineers to have entire control over their operations, bring their ideas to life.
Therefore, the team must prioritize ease-of-use when implementing minor features. Design language should be employed to simplify complex engineering concepts, making them understandable to a broader audience. User experience is more crucial than we ever imagined!
Engagement Platform is now an intricate system that manages user segmentation, incentives, notifications, and content. Ensuring a clear and collaborative user experience across these interconnected resources and functionalities is challenging.
💭 Consider a typical scenario: a promotion triggers emails and push notifications containing links to content within the platform. How can we effectively guarantee consistency in messaging across all these touchpoints?
The team is working on complex real-world applications and developing assistive tools to ensure consistency across diverse resources and streamline their alignment. However, this approach faces inefficiencies due to:
- The tension between the specificity required for consistency and the need for flexibility.
- The limitations of static analysis in identifying all inconsistencies, particularly in natural language information. Also, these static analysis tools introduce maintenance effort per use case, not very scalable and increase overhead over time.
These are tradeoffs the team continuously takes into consideration. With rapid growth of our business needs, the development effort to support it also scales rapidly as these all need engineers’ hands-on effort.
For example, introducing a new platform capability to users, usually involves several steps:
- Backend Service Readiness: The backend service must be developed to handle business logic and offer APIs for client-side interaction.
- Client-side Development and UX Design: This involves working with the product team to define the user experience and then implementing the necessary UI modifications within the application to make the functionality accessible to users.
Instead of making engineers build every little thing and cluttering the interface with a million buttons, wouldn’t it be cool if our tools could just talk to us?
Agentic UX: Let’s Make Our Tools “Talk”
So, yeah, Language Models (LLMs) are looking pretty tempting these days. The fact that they can actually understand what we’re saying is a definite plus. And hey, let’s be real, playing around with this new tech sounds kinda fun, right? 😄
Think about all those AI apps popping up that everyone’s using. Notice a pattern? It’s usually some kind of chat thing going on.
"Why Chat?"
Basically, "talking" to an LLM is like asking it for information using normal language. One of the cool things about this kind of interaction is that we don’t need to make a bunch of changes to how our tools look to add new stuff.
"The key is still how to efficiently and precisely let our users access what our service can do."
Remember when LLM apps were just starting out, and ChatGPT was the biggest thing? Even though LLMs couldn’t directly operate systems or data, people already started to "vibe something". They could give helpful advice, like step-by-step guides to get things done.
With the above ideas and observations in mind, we decided to introduce an Agent to our system. Aside from thinking about how humans can understand and use the tool, let’s focus on how Agent (AI) can understand and access it, because the investment has a very high return which brings these benefits:
- Lower the entry barrier: Our users can ask basic questions, know almost nothing to get started, because the Agent can give them instructions via Q&A iteration.
- Streamline complex tasks: Instead of clicking through endless menus or filling out lengthy forms, users can simply tell the Agent what they need. Think of it as having a super-smart assistant that anticipates your needs.
- Reduce development time: By letting the Agent handle some of the user interactions, we can reduce the amount of custom UI development needed. Plus, less hand-holding for every single new feature is a major win! (Busy platform team 🥵)
- Enhance user experience: A conversational interface can make using our tools feel more intuitive and less like wrestling with a computer. It’s like teaching our tools to speak our language, not the other way around.
- Increase flexibility: The Agent can adapt to different user needs and preferences on the fly, making our platform more versatile and user-friendly. We can even add new functionalities without needing to redesign the whole interface! (Who doesn’t love skipping a redesign meeting or two?)
After intensive development and workshops, our team brought the very first version of this Agentic UX into our platform. Here’s a quick peek into our progress!
From Rough Draft to Reality: Building an AI Assistant
From a quick glance, yeah, it might look like just another AI chat tool, and honestly, at first, that’s kinda what it was! It allows users to attach sources, check references, and even has a "thinking process" we designed ourselves. Pretty standard AI fare.
But here’s the catch – for us, just "pretty standard" wasn’t gonna cut it. We needed super high accuracy. If this thing messed up, it wouldn’t just be a minor glitch, it could be a major incident generator. Imagine accidentally sending out the wrong promotion to thousands of users! Not exactly a "oops, my bad" situation.
So, we went deep into the rabbit hole. Massive prompt engineering? Check. Implemented more guardrails than a bowling alley? Double check. Created new designs to connect the Agent seamlessly into our existing systems and UI? You betcha. It was like trying to teach a brilliant, but slightly chaotic, intern how to perfectly follow a super complicated set of instructions.
Achieving production-level quality with AI is far more than just "magic"; it demands significant engineering effort to ensure accuracy and reliability. It’s not enough for AI to simply talk; it must consistently say the right things to be a dependable tool.
Conclusion: Just the Tip of the AI-berg
So, this is definitely not the end of the story. In fact, it’s really just the beginning.
The whole AI world is changing everything around us, and we’re basically just learning how to swim in this new AI tide. We’re adapting, experimenting, and maybe splashing around a bit too much. But hey, you gotta start somewhere!
What we’ve really done here is open the door. We’ve built a foundation to bring the future of AI’s superpowers to our platform. We’re talking about AI that not only talks but understands, anticipates, and makes our tools smarter than we ever imagined. This first version of the Agent? It’s just the first step on a much longer, much more exciting journey. And we can’t wait to see where it takes us (and our users!).
Tomorrow’s article will be by @toshinao from the Mercoin Ops Team.. Look forward to it!
