Integrating AI into existing processes

Client

Multi App · Case Study

Year

2025

Disclaimer: product is in closed testing. Details on live calls.

Product: A messenger where AI agents join your real conversations. Not a separate "AI chat" tab. Agents sit inside existing dialogues, across platforms, alongside real people. Plus a marketplace of 200+ agents.

Problem: Slow development, too many bugs, no structure, no design system.

Cause: A huge product, a very small team, and the constant need to stitch together pieces from existing open source products.

Solution: AI prototyping from the earliest stages of brainstorming - covering core scenarios directly in code and handing off live, working prototypes to development instead of static mockups. The result: 80% shorter path from idea to implementation, by combining borrowed backend infrastructure with a code-first design approach.

Too many ideas, not enough runway

Small startup teams rarely take on projects this ambitious - usually they scope down to a simple MVP and move on. This was different. We got stuck.

The ask was almost impossible - build a messenger where users can talk to both people and AI agents. Also add access to other messengers and make it a unified hub for all platforms. Also connect a marketplace of 200+ agents and let them join existing conversations. Also implement audio and video calls. Also...well, you get it.

We hit a wall fast. Design and architecture was running weeks ahead of development - months of work in a single week. Something had to change.

So we changed it.

What made this possible

I built the system. It took a while and it hurt. But it works.

A complex combination of MCP, API keys, rules, skills and a base design system that didn't come together overnight. But now it lets us solve an enormous number of development problems that used to slow everything down.

Prototypes come out as fully functional mini-products from the start - with access to a mock database, full understanding of the entire product context, and real data and systems to work with.

Components are built in code and mapped automatically. No more walls of incoherent AI-generated frontend code that makes no sense and has to be rewritten from scratch.

Real products, assembled and tested, running through actual user research platforms with real users.

And the most important part - the ability to experiment at almost zero cost of failure.

There's a lot more to this. Happy to walk through it on a call.

Got something to build?
Let's not just talk about it.

Got something to build?
Let's not just talk about it.

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