Why We Built Zinochat - Taking a Different Approach to AI Customer Experience

In early 2026, we were a very small and flat team. Every member had spent years working in e-commerce, building products and solving problems in the industry.

When the AI wave arrived, we started thinking deeply about what the future of customer experience could look like. As we explored the market, we realized that there was still no AI CX platform that truly addressed the complexity of e-commerce while also being deeply localized for the markets we understood.

That was the reason we founded Zinochat.

E-commerce Is More Complex Than It Looks

Customer experience in e-commerce is not just about replying to messages or running customers through predefined support scripts.

An agent needs to understand products, variants, pricing, promotions, inventory, orders, shipping, customer history, and business rules. It needs to support multiple channels and handle thousands of different situations. On top of that, every market has its own language, customer behavior, and business practices.

A model can be extremely capable and still fail when it is put into this environment.

We looked at the market and couldn’t find an AI CX platform designed deeply enough around the complexity of e-commerce, especially for local markets. So we decided to build one ourselves. Not another chatbot. Not simply another interface on top of an LLM.

We wanted to build the foundation that allows AI agents to operate reliably in the real world.

We Are Not Rushing to Launch Zinochat to the Mass Market

There was a lot to build, and we wanted to approach it carefully. So we deliberately chose to start small.

We didn’t spend money on advertising. We didn’t have a sales or business development team. Fortunately, after many years in the industry, some of our existing connections became Zinochat’s first customers. Instead of trying to acquire as many customers as possible, we worked closely with a small group of them.

We watched how their agents behaved in real conversations. We looked at failures, edge cases, unexpected behaviors, integration problems, and everything in between.

Our customers gave us an incredible amount of feedback. That feedback became one of Zinochat’s most valuable assets. It allowed us to identify and address problems early, and to improve the product much faster than we could by simply chasing customer growth. We chose to spend our resources selecting and refining the features customers genuinely needed instead of supporting a large customer base from the outset.

That’s also why we haven’t rushed toward a broad public launch. We believe capability should come before growth. Our existing customers need reliable infrastructure and strong support. Before bringing Zinochat to a much larger audience, we want to make sure the platform can truly meet those expectations.

A Good Agent Is Not the Agent That Does Everything

There is a lot of pressure in the AI industry to keep adding capabilities.

• More tools.
• More integrations.
• More autonomy.
• More features.

But we don’t believe an agent becomes better simply by doing more.

A good agent is not the agent that can do everything. A good agent is the agent that can do the right things reliably. In production, predictability matters. An agent needs a strong foundation that allows it to behave consistently, recover from failures, adapt to different business requirements, and improve over time.

That’s why we have spent so much time on the parts of the system that users don’t always see:

• How the agent retrieves and understands knowledge
• How it decides when and how to use tools
• How it handles business rules and edge cases
• How we evaluate its responses and actions
• How customers can observe and adjust its behavior
• How an agent can be tested and tuned before and after deployment
• How the entire system can remain stable as usage grows

These things may not be as exciting as adding another feature to a demo.

But they are what make an AI agent actually usable in production.

From Building Agents to Building the Agent Platform

After months of work, the major components of Zinochat are now in place.

We’ve reached a much more stable stage of the product, and our focus is shifting from simply building capabilities to improving the entire agent development lifecycle. We are building tools that help customers, developers, and specialists create, integrate, evaluate, measure, and continuously tune their agents.

The goal is simple:

Close the gap between a human team member and an agent that can serve customers reliably every day.

This means understanding not only what an agent did, but why it did it, where it failed, and how to improve it.

We also want to make integration much easier.

E-commerce businesses already have their own systems, data, workflows, and sales channels. We don’t want to build custom modules from scratch every time an AI agent is introduced. That’s why we are creating a platform that makes it easier than ever to adapt across different business models.

Zinochat should handle most of that complexity.

The Agent-Building Era Is Just Beginning

Something else is changing at the same time.

Coding agents such as Claude and Codex are making software development dramatically more accessible.

As this trend continues, building an AI agent will no longer be something that only a specialized AI engineering team can do. More non-technical users will be able to create, customize, and improve their own agents. But making an agent easy to create is only half of the problem.

The harder half is making it stable and trustworthy.

As more people can build agents, we believe the need for better tooling around evaluation, observability, testing, integration, and continuous improvement will become even more important. This is the direction we want Zinochat to take.

Not just a place where you create an agent, but a platform where you can build, test, measure, improve, and operate agents in the real world.

What’s Next

We still have a long way to go.

There are many hard problems ahead, and we know that the platform will continue to evolve as we learn from our customers and from the rapidly changing AI ecosystem. For now, we will continue to focus on a relatively small group of customers, strengthen the foundation, and improve the customer experience.

We started Zinochat with a small team and a simple belief:

The future of AI CX will not be won by the agent with the most features. It will be won by the platform that makes agents reliable, adaptable, and genuinely useful in the real world. We don’t know exactly how far we can take it. But that is what makes this journey worth pursuing.

Let’s see how far we can go.