The Problem
Senior architects and tech leads waste 2-3 weeks debating system design before writing a single line of code. They are not debating syntax. They are debating architectural decisions:
- Should we use async job queues or sync processing?
- How many read replicas do we actually need?
- Does this tech stack even work together?
- Where will this fail under load?
The worst part? These decisions get validated after they start coding. Months later, in production, they discover the architecture does not handle what they thought it would.
The Idea
What if we could validate architecture before building it?
Not by having more meetings. Not by drawing diagrams in Miro. But by encoding architectural rules and running descriptions against them.
So I built a system that:
- Takes a plain English system description
- Validates it against 50+ architectural rules
- Generates a production-ready blueprint with diagrams, tech recommendations, and implementation scaffolds
How It Works
Step 1: Parse the Description
User inputs something like:
Multi-tenant SaaS platform for project management.
Stripe payments, async job queue for notifications,
read replicas for scaling. Expecting 10k DAU by month 6.
Step 2: Validate Against Rules
The system runs this against a ruleset:
Rule: If async jobs exist -> queue system required
Rule: If 10k DAU -> caching layer required
Rule: If Stripe -> PCI compliance validation
Rule: If multi-tenant -> data isolation validation
Rule: If read replicas -> consistency strategy required
It is not regex matching. It is semantic validation using Claude's API to understand the implications of each architectural decision.
Step 3: Generate the Diagram
Once validated, it generates a Mermaid.js diagram showing component relationships, data flow, failure points, and scaling bottlenecks.
Step 4: Generate Tech Stack and Scaffolds
Based on the description, it recommends a concrete tech stack and generates starter code for critical components like Stripe webhook handlers, job queue setup, and database initialization scripts.
Why This Matters
Traditional architecture tools make you:
- Draw boxes
- Connect lines
- Hope you did not miss anything
Archivolt validates before you draw. It catches anti-patterns:
- Using Firestore with strong consistency requirements
- 100k users on a single database server
- Multi-tenant app sharing databases across tenants
And suggests fixes:
- Switch to PostgreSQL with row-level security
- Add a read replica and cache layer
- Implement schema isolation strategy
What Makes This Different
vs. Lucidchart and draw.io: Drawing tools only. No validation.
vs. C4 model tools: Great for documenting architecture, but you still have to know what is right.
vs. ChatGPT: Asking "is this a good architecture?" gives 50 different answers. Archivolt has consistent rules.
vs. Infrastructure-as-Code: IaC is for implementation. This is for validation, which happens first.
Current State
Shipped last week. Early feedback from users:
- Staff engineers using it to validate proposals before team debates
- Architects catching mistakes before 2 weeks of coding
- CTOs using it to teach junior architects system design
Free to try (1 generation). Unlimited blueprints on Pro.
What Is Next
Working on:
- Terraform and CloudFormation export: go from blueprint to infrastructure code
- Deterministic validation layer: stricter rules, fewer hallucinations
- Team collaboration: shared blueprints with comments
- Pattern library: pre-built solutions for common architectures
Try It
archivolt.dev - describe your system, get a blueprint in 60 seconds.
Would love to know: what breaks it? What patterns would help your team? Would you use the Terraform export?
Drop a comment or join the Discord community (link on the site).
I am a solo founder in Lahore, shipping this in public. If you build with Archivolt, I would love to hear what you are working on.
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