Live Production Demo • Architecture Analysis • Hybrid AI / Deterministic Workflows
🔗 Live Demo: https://archflow-sigma.vercel.app/
ArchFlow is an AI-assisted platform for architecture analysis and decision support. It demonstrates how modern engineering teams can automate documentation, detect architectural risk, and generate traceable recommendations—while keeping AI observable, auditable, and constrained to the right parts of the workflow.
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Architecture Decision Support AI-assisted recommendations with clear rationale, trade-offs, and context.
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Hybrid Analysis Pipeline Deterministic static analysis combined with AI synthesis—no black boxes.
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Traceable & Auditable Workflows Every insight maps back to source code and deterministic rules.
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Repository Insights Dashboard Live metrics for tech debt, dependency risk, security signals, and performance.
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Enterprise-Ready Design Built with observability, compliance, and scalability in mind.
ArchFlow is built around a simple principle:
AI should augment engineering judgment, not replace it.
Deterministic analysis is used wherever possible. AI is applied only for higher-order reasoning—such as pattern recognition, synthesis, and recommendation generation—and its outputs remain fully traceable.
Analysis Pipeline
- Code Ingest — Repository sync and parsing
- Static Analysis — Dependency mapping and signal extraction
- AI Reasoning — Pattern recognition and insight synthesis
- Rule Validation — Deterministic compliance and sanity checks
- Output Generation — Reports, insights, and actionable recommendations
Each stage is observable, isolated, and designed for auditability.
- Frontend: Next.js 14 (App Router), TypeScript
- Styling: Tailwind CSS
- UI: Lucide React
- Deployment: Vercel
- Architecture: Monorepo, component-driven design
🛠️ Recent Improvements & Challenges Addressed
Over the past development cycle, ArchFlow has undergone significant enhancements to improve reliability, usability, and production readiness:
Improved Authentication and User Experience: Login flows now ensure users are prompted only when necessary, with secure, context-aware authentication for seamless access to architecture insights.
Environment-Aware API Integration: Backend endpoints and CORS policies have been aligned for consistent behavior across environments, enhancing stability and reducing integration issues.
Enhanced Observability and Documentation: Dashboards and insights are fully traceable and auditable, while metadata and social sharing support improve platform visibility and engagement.
Robust Monorepo and Scalable Architecture: The codebase is structured into frontend, API, and documentation packages, supporting scalable, component-driven development and future feature expansion.
Polished UX and AI Integration: AI-assisted recommendations remain clear, contextual, and traceable, while deterministic analysis ensures outputs are safe and auditable.
These updates reflect a commitment to enterprise-grade software engineering, balancing advanced AI capabilities with maintainable, scalable architecture.
git clone https://github.com/QuisTech/archflow
cd archflow/packages/web
npm install
npm run devOpen http://localhost:3000 to view the dashboard.
archflow/
├── packages/
│ ├── web/ # Next.js 14 dashboard
│ ├── api/ # (Planned) Backend services
│ └── docs/ # (Planned) Documentation
└── infrastructure/ # (Planned) Deployment & IaC
- End-to-end system design with a long-term architecture vision
- Practical application of LLMs in production-style workflows
- Clear separation between AI reasoning and deterministic logic
- Enterprise-grade thinking around observability and compliance
- Polished UX for complex technical insights
- Live Demo: https://archflow-sigma.vercel.app/
- Source Code: https://github.com/QuisTech/archflow
MIT License — see LICENSE for details.
ArchFlow explores the intersection of AI-assisted tooling, software architecture, and production-grade engineering practices.