System design prep that
builds your mental model.
40 hours of YouTube and you still freeze when the interviewer asks you to design something. Aurora teaches through dialogue, then you practice. Functional requirements, non-functional requirements, and capacity estimation across six progressive sets, before the canvas ever opens.
What is Aurora AI?
Aurora is Levelop's AI system design mentor built for FAANG interview preparation. Instead of giving you a blank canvas and a problem statement, Aurora starts with a voice-narrated knowledge session that walks you through each concept with checkpoint quizzes. Then you practice (working through functional requirements, non-functional requirements, and capacity estimation in a dedicated workspace). Each problem is spread across six progressive sets that deepen your understanding from different angles. Only after you've built a complete mental model does the interactive canvas unlock, where you design real architectures with load balancers, databases, caches, and message queues. This approach builds the ability to reason about system design from first principles, exactly what interviewers at Google, Meta, and Amazon are looking for.
How Aurora Works
- 01
Aurora teaches through voice dialogue
Each system design problem starts with an Aurora knowledge session. Aurora walks you through the core concepts via live voice narration. Not a pre-recorded video, but an adaptive dialogue with checkpoint quizzes that verify your understanding at every stage.
- 02
Practice what Aurora taught you
After the knowledge session, you practice in the system design workspace. Each problem is broken into three phases (functional requirements, non-functional requirements, and capacity estimation) so you build a complete mental model, not just surface familiarity.
- 03
Six sets deepen your understanding
Each problem is spread across six progressive sets. You revisit the same system from different angles. Each set pushes you deeper into the trade-offs, failure modes, and scaling decisions that interviewers actually ask about.
- 04
Canvas unlocks for architecture design
Once you've completed the practice phases, the interactive canvas opens. Build real system architectures: load balancers, caches, databases, message queues, on a node-based editor. The same problems that appear at Google, Meta, and Amazon.
Real System Design Challenges
The same problems that appear in FAANG interviews. Each starts with an Aurora knowledge session, progresses through requirements and capacity practice across six sets, then opens the canvas.
URL Shortener
Hash functions, distributed ID generation, read-heavy optimization.
News Feed
Fan-out strategies, caching layers, ranking algorithms.
Messaging System
WebSocket connections, message queues, delivery guarantees.
Distributed Cache
Consistent hashing, eviction policies, cache invalidation.
Streaming Platform
CDN architecture, adaptive bitrate, content delivery.
Rate Limiter
Token bucket, sliding window, distributed rate limiting.
And 24 more patterns covering search engines, payment systems, notification services, and beyond.
How is Aurora different from system design courses?
Watch someone else design a system
Passive, you follow along but can't construct a design from scratch under pressure
Give you a problem and a drawing board
No guidance, you freeze because you haven't built the mental model first
Teaches through voice dialogue, then you practice functional/non-functional/capacity estimation across 6 sets before the canvas unlocks
Active understanding: learn, practice, and design. By the time you touch the canvas, you own the mental model
Start your first sprint free.
Aurora AI is included from day one. No credit card required.
- First sprint free
- No credit card
- Cancel anytime