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Solarch

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Solarch is an architecture-first development tool that turns backend diagrams into validated, code-generating graphs. Developers can draw services and relationships, let the rules engine reject invalid architecture, and generate code that stays aligned with the design.

Added on June 18, 2026

Solarch Screenshot

Product Information

What is Solarch?

Solarch is a developer tool for designing backend architecture before code is generated. It represents systems as live node-and-edge diagrams, validates relationships with a rules engine, and uses AI to fill implementation bodies after the architecture is constrained. The product focuses on reducing architectural drift by keeping diagrams, validation, generated code, and documentation connected. It is aimed at developers and teams experimenting with AI-assisted backend development but still wanting explicit structure and reviewable architecture.

How to use Solarch?

  1. Open Solarch and create a new backend architecture graph.
  2. Add nodes for services, modules, APIs, data stores, or other backend components.
  3. Connect nodes with typed edges and let the rules engine flag invalid relationships.
  4. Generate deterministic code skeletons and use AI assistance for implementation details.
  5. Export or document the resulting architecture so the code and diagram stay synchronized.

Core Features

  • Architecture canvas — Lets teams draw backend systems as live node-and-edge graphs.
  • Rules-based validation — Rejects invalid relationships before they become code debt.
  • Code generation engine — Produces skeletons and implementation support from the validated graph.
  • AI-assisted implementation — Uses AI after architectural constraints are already defined.
  • Semantic zoom — Supports high-level modules and lower-level micro-graphs in one model.
  • Vector export — Turns architecture into AI-readable context for other development tools.
  • Autonomous QA tools — Adds red-team checks, seed data, and snapshots around generated systems.

Use Cases

  • Backend planning — Model services, APIs, and data relationships before writing implementation code.
  • AI coding guardrails — Give AI tools a validated architecture instead of loose prompts.
  • Architecture documentation — Keep diagrams and generated code aligned for future maintenance.
  • Prototype generation — Move from a structured backend sketch to a working codebase faster.

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