AI Architecture Review
Describe your architecture, stack, workload, and requirements. Get a score, bottlenecks, unnecessary complexity, security risks, cost risks, and a recommended architecture.
An AI architecture review evaluates orchestration, model deployment, data layer, and state management against your actual workload, latency, compliance, and team capacity to find bottlenecks and unnecessary complexity before they become expensive to fix.
Architecture Score / 100
Well-Architected
Recommended Architecture
Your architecture is well-matched to what you described. Keep observability in place as workload and requirements grow.
How it works
Describe your orchestration and deployment
Choose your orchestration pattern (single call, RAG, single agent, multi-agent) and how models are deployed.
Describe your data and state layer
Select your retrieval setup and how session state or memory is managed.
Set your requirements
Enter workload volume, latency needs, compliance sensitivity, observability, and team capacity.
Get your score and risks
The review runs your answers against a fixed rule set and returns a 0-100 score, tier, and a categorized list of bottlenecks, unnecessary complexity, security risks, and cost risks.
Read the recommended architecture
The highest-impact fixes are summarized into a concrete recommendation for what to change first.
When should I use this?
- You're preparing an AI system for production and want a second look at the design.
- The architecture grew organically and you're not sure what's load-bearing versus accidental.
- Several models, providers, or retrieval sources are involved and no one's traced how they interact.
- Latency is increasing or agent workflows are getting harder to debug.
Who is this for?
- Engineering leads validating an architecture before it scales.
- AI and ML engineers inheriting a system someone else built.
- CTOs deciding whether to invest in a rebuild or a fix.
Common Questions