# corning-ai/caid architecture

> Architecture diagram of the GitHub repository corning-ai/caid, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-17). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/corning-ai/caid
- Repository: https://github.com/corning-ai/caid

## Overview

1. **Repository state**
   - This repo is currently a **documentation/landing repository**, not an implemented CAD system.
   - The only tracked content is `README.md`, `LICENSE`, and a branding asset in `assets/logo.png`.

2. **What is actually present**
   - `README.md` defines the project vision, feature set, and a proposed architecture.
   - `assets/logo.png` is purely presentation/branding.
   - There is **no source code, package structure, tests, build config, or runtime entrypoint** in the current tree.

3. **Current architectural boundary**
   - The repo boundary is effectively:
     - **Public project documentation** on one side
     - **Future application implementation** on the other
   - Any real system diagram should therefore distinguish between the **documented intended system** and the **nonexistent implementation in this repo**.

4. **Intended top-level system**
   - The README describes CAiD as an **AI-driven CAD modeling agent** that converts natural language concepts into parametric 3D CAD models.
   - It is positioned as a pipeline that moves from **design intent** to **model generation**, then to **validation/optimization**, and finally to **export**.

5. **Proposed functional subsystems from the README**
   - **Natural Language Processing Engine**: interprets user requirements and design intent.
   - **Parametric Modeling Core**: constructs and updates the CAD model.
   - **Constraint Solver**: enforces dimensional/geometric rules.
   - **Optimization Module**: improves manufacturability or design quality.
   - **Export Engine**: outputs industry-standard CAD/mesh formats.

6. **Intended data flow**
   - User design description → NLP interpretation → structured design intent/parameters → parametric model generation → constraint solving → optimization/refinement → export.
   - The README also implies an **iterative loop** where model changes and optimization feed back into the parametric core.

7. **External integration points implied by the README**
   - Export targets include **STEP, IGES, STL, and BREP**, which means the eventual implementation would need geometry kernel/export-format support.
   - The README mentions compatibility with **FEA/CFD analysis tools**, implying future integration boundaries for validation and simulation workflows.
   - No such integrations are implemented in the current repo.

8. **User-facing surfaces implied, but not yet built**
   - The README references a future **Python 3.8+** environment and mentions both **basic usage** and **CLI documentation** as “coming soon.”
   - That suggests the eventual system may expose at least:
     - a Python API
     - a command-line interface
     - possibly higher-level workflow orchestration for model generation

9. **Development and contribution posture**
   - The repo is structured as an open-source project on GitHub with MIT licensing.
   - Development guidelines in the README indicate a future Python codebase with PEP 8 style and unit tests, but those conventions are aspirational because no code exists yet.

10. **What an architecture diagram should show**
    - For the current repo: a **documentation-only shell** with branding and license.
    - For the intended product: a **concept-to-CAD pipeline** with NLP, modeling, constraint solving, optimization, validation, and export stages.
    - Make it explicit that the second diagram is **planned architecture**, not a reflection of present implementation.

11. **Practical takeaway**
    - There are no deployable services, modules, queues, databases, or model artifacts to diagram today.
    - The only defensible architecture description is a **future-state CAD AI pipeline described in README prose**, backed by no code in the repository yet.

Main entry points: [`README.md`](https://github.com/corning-ai/caid/blob/main/README.md) (README), [`LICENSE`](https://github.com/corning-ai/caid/tree/main/LICENSE) (MIT license)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_current_repo["Current repo"]
  node_repo_boundary["Repo shell<br/>docs-only"]
  node_readme["README<br/>documentation<br/>[README.md]"]
  node_license["MIT license"]
  node_logo["Logo<br/>branding asset"]
end

subgraph group_planned_system["Planned system"]
  node_workflow(("Workflow<br/>orchestration"))
  node_nlp["Intent parser<br/>nlp"]
  node_intent_schema["Design intent<br/>structured spec"]
  node_modeling["Model core<br/>parametric CAD"]
  node_constraints{{"Constraint solver<br/>geometry rules"}}
  node_optimization["Optimizer<br/>refinement"]
  node_export[("Export engine<br/>output formats")]
  node_analysis["Analysis tools<br/>external integration"]
  node_python_api["Python API<br/>user interface"]
  node_cli["CLI<br/>user interface"]
end

node_readme -->|"documents"| node_repo_boundary
node_repo_boundary -->|"contains"| node_logo
node_repo_boundary -->|"contains"| node_license
node_readme -->|"describes"| node_workflow
node_workflow -->|"starts with"| node_nlp
node_nlp -->|"produces"| node_intent_schema
node_intent_schema -->|"drives"| node_modeling
node_modeling -->|"checked by"| node_constraints
node_constraints -->|"refines"| node_optimization
node_optimization -.->|"iterates"| node_modeling
node_optimization -->|"outputs to"| node_export
node_export -.->|"integrates with"| node_analysis
node_readme -.->|"implies"| node_python_api
node_readme -.->|"implies"| node_cli
node_workflow -.->|"driven by"| node_python_api
node_workflow -.->|"driven by"| node_cli

click node_readme "https://github.com/corning-ai/caid/blob/main/README.md"
click node_license "https://github.com/corning-ai/caid/tree/main/LICENSE"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_repo_boundary,node_readme,node_license,node_logo toneBlue
class node_workflow,node_nlp,node_intent_schema,node_modeling,node_constraints,node_optimization,node_export,node_analysis,node_python_api,node_cli toneAmber
```

## Components

### Current repo

Documentation-only repository with branding and license; no implementation code is present.

- **Repo shell**: docs-only · Current repository boundary: documentation and assets only, with no runtime or package structure.
- [README](https://github.com/corning-ai/caid/blob/main/README.md): documentation · Primary source of project vision, feature set, and proposed architecture. · `README.md`
- [MIT license](https://github.com/corning-ai/caid/tree/main/LICENSE): license · Open-source licensing for the project. · `LICENSE`
- **Logo**: branding asset · Presentation asset used for project branding.

### Planned system

Future-state CAiD architecture described in the README, not implemented in this tree.

- **Workflow**: orchestration · Planned concept-to-CAD pipeline that coordinates intent parsing, modeling, validation, and export.
- **Intent parser**: nlp · Interprets natural-language design descriptions into structured intent and parameters.
- **Design intent**: structured spec · Intermediate representation of goals, parameters, and constraints extracted from user input.
- **Model core**: parametric CAD · Builds and updates the parametric CAD model from structured design intent.
- **Constraint solver**: geometry rules · Enforces dimensional and geometric constraints on the evolving model.
- **Optimizer**: refinement · Improves manufacturability, quality, or other design goals before export.
- **Export engine**: output formats · Produces STEP, IGES, STL, and BREP outputs for downstream CAD and mesh workflows.
- **Analysis tools**: external integration · Future boundary for FEA/CFD compatibility and validation-oriented workflows.
- **Python API**: user interface · Planned programmatic surface for driving model generation from Python 3.8+ environments.
- **CLI**: user interface · Planned command-line surface referenced as coming soon in the documentation.

## Connections

- README → Repo shell: documents · The README defines the project vision for a repo that currently contains no source code.
- Repo shell → Logo: contains · Current tree includes branding assets only.
- Repo shell → MIT license: contains · Current tree includes project licensing.
- README → Workflow: describes · The documentation presents the intended concept-to-CAD pipeline.
- Workflow → Intent parser: starts with · User intent is interpreted into structured design parameters.
- Intent parser → Design intent: produces · Natural language is normalized into a structured design-intent representation.
- Design intent → Model core: drives · Structured intent feeds the parametric CAD construction core.
- Model core → Constraint solver: checked by · Generated geometry is constrained and validated.
- Constraint solver → Optimizer: refines · Constraint-respecting models are improved for quality or manufacturability.
- Optimizer → Model core: iterates · Optimization can feed back into the parametric core for another modeling pass.
- Optimizer → Export engine: outputs to · Finalized geometry is exported to downstream CAD/mesh formats.
- Export engine → Analysis tools: integrates with · Exported artifacts are intended for external FEA/CFD and similar analysis tools.
- README → Python API: implies · The future implementation is expected to expose a Python-facing workflow.
- README → CLI: implies · The documentation mentions CLI usage as a future surface.
- Workflow → Python API: driven by · The planned orchestration can be invoked through a Python API.
- Workflow → CLI: driven by · The planned orchestration can also be invoked from the command line.