Technical

Design System Architecture with AI-Powered Mind Maps

Use AI mind maps to explore architectural decisions, compare trade-offs, and map system dependencies visually before writing code.

Design your architecture See example ↓

Explore a reference map

The whole architecture of Redis in one map, zoomed out. Zoom into any subsystem, persistence, clustering, the event loop, to read how it works.

Open the full map

How it works

Architecture decisions are rarely linear. Choosing a database affects your caching strategy. Choosing an API style affects your frontend complexity. Mind maps let you see these dependencies as a connected tree rather than a flat document.

Start with your system requirements. Describe what you’re building. The AI maps out the major components, boundaries, and communication patterns.
Branch into decisions. Each architectural choice becomes a fork. “Should we use REST or GraphQL?” generates branches with trade-offs for each option. You explore both without losing context.
Map dependencies. When you see that choosing microservices affects your deployment pipeline AND your team structure, that’s visible as interconnected branches — not buried in paragraph 47 of a design doc.
Converge on a design. Prune the branches that don’t survive trade-off analysis. What remains is an architecture that’s been stress-tested against alternatives.

Why branching matters for architecture

The Architecture Decision Record (ADR) format acknowledges that every technical choice has alternatives. But ADRs are written after the decision. Mind maps let you explore alternatives before committing — and the visual layout makes it obvious when decisions conflict or depend on each other.

This is especially valuable for distributed systems, where a choice in one component cascades through the entire stack.

Example

You’re designing a notification system. The root branch splits into “push vs pull,” “real-time vs batched,” and “per-user vs topic-based.” Under real-time push, you fork into WebSockets vs SSE vs long-polling. Under per-user, you explore fan-out strategies. The mind map reveals that combining real-time + per-user at scale requires a message broker — a dependency that’s invisible in a linear design doc.

Explore related technical workflows like API design, database design, or migration planning for adjacent architectural decisions.

Now try it yourself

Design the architecture for a real-time collaboration app

You'll need WebSocket-based communication, a CRDT or OT layer for conflict resolution, and a persistence layer that handles eventual consistency.

What are the trade-offs between CRDTs and OT?

CRDTs are simpler to reason about and work peer-to-peer, but use more bandwidth. OT is more established for rich text but requires a central server for transformation ordering.