The AI Architectural Linter
A per‑repository micro‑model that understands your project’s architecture, patterns, and intent - and protects it from drift, rot, and inconsistency.
The Problem: AI Drift
AI accelerates development, but it also accelerates architectural drift. Inconsistent patterns, duplicated logic, and context‑free code generation are becoming the new technical debt.
Context Limits
Even the best AI models only see a slice of the codebase. They lack persistent, repo‑specific memory and cannot enforce long‑term structure.
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Inconsistent Patterns
Different agents and developers introduce different approaches. Naming conventions drift, and modules evolve independently instead of cohesively.
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Review Overload
Senior engineers spend too much time policing patterns and explaining architectural decisions that should be self‑evident.
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The Solution: A Per‑Repo Micro‑Model
ArchGuardian introduces a project‑specific AI model that lives inside your repository and evolves with every change.
Micro‑Model Per Repository
Small enough to train locally (1–3B), but large enough to encode patterns, naming, and architecture.
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LoRA Updates on PR Merge
A GitHub Action fine‑tunes the model using the diff, context, PR description, and review comments.
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Scheduled Rebuilds
Nightly or weekly jobs rebuild the model from scratch to prevent drift and ensure full context.
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Why This Works
- ✓ Persistent Context: The model remembers architectural decisions.
- ✓ Privacy First: Models stay in your repo; no cross‑tenant training.
- ✓ Agent Agnostic: Works with Copilot, Claude, Cursor, or any MCP tool.
- ✓ Economically Viable: Client‑side training means no massive hosting costs.
System Architecture
How ArchGuardian integrates into your existing workflow.
1. Base Model
Standard 1-3B parameter model suitable for LoRA.
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2. Fine-Tuning
GitHub Actions run LoRA updates on every PR merge.
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3. Inference
Agents query the model via MCP for architectural guidance.
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Example Interactions
See how ArchGuardian acts as an intelligent architectural assistant.
Pattern Detection
"Where is OAuth implemented in this repo?"
> Pattern: Custom middleware
> Entry point: AuthConfig.java
> Used in: gateway, user-service
Impact Analysis
"What breaks if I change UserDTO?"
> 2 API endpoints need updates
> 5 Test suites will fail
Architectural Linting
"Is this PR consistent with our event handler pattern?"
> Expected: [Domain]Event
> Found: [Domain]Message
Canonical Guidance
"How do I add a new event handler?"
> Extend BaseEvent.
> Register in EventRegistry.java.
FAQ
Why not just use embeddings or RAG?
Embeddings provide retrieval, not understanding. They cannot learn patterns, enforce architecture, or evolve with the codebase in the same way a fine-tuned model can.
Is this secure?
Yes. Models are stored inside your repository. Training happens locally or on self‑hosted runners. There is no cross‑tenant training or centralised model hosting.
Does this replace Copilot?
No. ArchGuardian is a context provider. It enhances Copilot, Claude, and other agents by giving them the deep, project‑specific architectural intelligence they currently lack.