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AI Architect

Transform Artifacts into Developer-Ready Documentation

Your Intelligent Documentation Assistant

AI Architect is a desktop application that automatically transforms scattered project artifacts—wireframes, user stories, meeting notes, technical specs—into comprehensive, production-ready Product Requirements Documents (PRDs) and Implementation Specifications.

Built by Certified Scrum Trainer Rod Claar using Test-First AI Development methodology, AI Architect eliminates documentation bottlenecks while maintaining the rigor and traceability enterprise teams require.

Get Started Today See How It Works

The Problem We Solve

Every software project generates artifacts across multiple tools: Jira tickets, Slack conversations, email threads, Google Docs, design files, and meeting notes. When it's time to create formal documentation, you face:

Hours of manual consolidation copying and organizing scattered information
Missing requirements buried in conversation threads
Inconsistent documentation with gaps and contradictions
Lost traceability between requirements and implementation
Outdated specs that lag behind development reality
Context switching between gathering artifacts and writing documentation

AI Architect solves this by intelligently importing, analyzing, validating, and generating complete documentation from your existing project artifacts.

Core Features

🗂️

Multi-Project Management

  • Manage multiple documentation projects in one workspace
  • Switch contexts instantly between active projects
  • Archive completed projects while maintaining full history
  • Track artifacts, requirements, and validation status per project
  • Last activity timestamps and project health monitoring
📥

Smart Artifact Import

  • Flexible Import Methods: Single file, entire directories, or multiple selected files
  • Intelligent Pattern Matching: Define include/exclude patterns with glob syntax
  • Automatic Type Detection: AI identifies artifact types with confidence scoring
  • Wide Format Support: Markdown, JSON, YAML, TXT, PDF, feature files, and more
  • Source Location Control: Import from anywhere in your file system
📊

Real-Time Dashboard

  • At-a-Glance Status: See imported artifacts, analysis progress, and validation issues instantly
  • Quick Actions: One-click access to entire workflow
  • Recent Activity Feed: Know exactly what happened and when
  • Auto-Refresh Metrics: Status updates automatically when new artifacts arrive
  • Workflow Guidance: Clear next steps based on current project state
📋

Artifact Catalog

  • Browse all imported artifacts with filtering by title, type, or dependency
  • Check import status (IMPORTED, PENDING, FAILED)
  • View dependencies between artifacts
  • Quick actions: Open, Re-ingest, View Metadata
  • Last imported timestamps for version control
🔍

AI-Powered Analysis

  • Semantic extraction of requirements from natural language
  • Automatic categorization (functional, non-functional, security, performance)
  • Priority assignment based on context
  • Source traceability to original artifacts
  • Relationship detection between requirements
✅

Comprehensive Validation

  • Gap Detection: Missing information, incomplete requirements, undefined terms
  • Conflict Identification: Contradictory requirements or specifications
  • Duplicate Detection: Similar or redundant requirements across artifacts
  • Severity Classification: Critical, Major, Minor, Possible Duplicate, Info
  • Validation Reports: Full findings summary with recommendations
📄

Dual-Mode PRD Generation

Template-Based Generation (Default):

  • Fast, structured, consistent output
  • Predictable section organization
  • No API calls required—works offline
  • Perfect for standardized projects
  • Ideal for compliance-heavy environments

LLM-Based Generation (Intelligent):

  • Narrative-style requirements (no rigid templates)
  • Context-aware synthesis of complex information
  • Custom instruction support for tone, style, and focus
  • Understands relationships between requirements
  • Perfect for novel, complex projects
📐

Technical Specification Generation

  • Customizable Depth: High Level, Detailed, or Comprehensive
  • Flexible Section Selection: Architecture, API Contracts, Data Models, Sequence Diagrams
  • Code Examples: API request/response, data schemas, SQL queries
  • Diagram Support: Mermaid, PlantUML, or both
  • Governance Controls: PRD approval required before generation
  • Estimated ~72 pages of implementation-ready documentation
🎛️

Complete AI Configuration Control

  • Provider Flexibility: OpenAI-compatible API endpoints
  • Use LM Studio, Ollama, or any compatible host
  • Local or cloud deployment options
  • Model Selection: Choose any available model (GPT-4, Claude, Llama, Qwen, etc.)
  • Fine-Tuned Parameters: Max Tokens, Temperature, Timeout
  • Security: API keys stored securely in system keychain
👁️

Prompt Preview & Transparency

  • See the exact prompt being sent to the LLM
  • Review system prompt with all customizations
  • Check template structure and sections
  • Verify model configuration
  • View estimated token count before generation
  • Copy prompt to clipboard for external refinement
  • No black-box AI operations
🎯

Custom System Instructions

  • Healthcare: HIPAA compliance language
  • Finance: SOC 2, regulatory requirements
  • Manufacturing: ISO standards, quality management
  • Government: Federal contracting terminology
  • Embedded Systems: Real-time constraints, hardware interfaces
  • Multi-Stakeholder Support: Same artifacts, different outputs
  • Total Customization: Your domain, audience, and standards

How It Works

1

Create Project

Set up a new documentation workspace with project name, path, and description. Projects can use absolute paths or workspace-relative paths for flexibility.

2

Import Artifacts

Configure source location and file patterns. AI Architect scans your project directory, detects artifact types automatically, and presents a preview before importing.

3

Analyze

AI extracts requirements from your artifacts using semantic analysis. Requirements are categorized, prioritized, and linked back to source files for full traceability.

4

Validate

The system checks for gaps, conflicts, duplicates, and incomplete information. Review validation findings by severity level and resolve critical issues.

5

Generate PRD

Choose template-based or LLM-based generation. Configure document metadata, add custom instructions, preview the prompt, and generate a comprehensive Product Requirements Document.

6

Generate Specification

After PRD approval, configure technical depth, select sections, choose diagram formats, and generate a detailed Implementation Specification for your engineering team.

7

Review & Iterate

Open generated documents in your preferred editor. If requirements change, re-ingest affected artifacts and regenerate—AI Architect maintains consistency across the workflow.

Technical Architecture

Desktop Application

  • Cross-Platform: Electron-based desktop app for Windows, macOS, and Linux
  • Python Backend: Robust CLI with comprehensive generation engine
  • SQLite Database: Local storage for projects, artifacts, and requirements
  • React UI: Modern, responsive interface with real-time updates

AI Integration

  • OpenAI-Compatible API: Works with any compatible endpoint
  • Local Model Support: LM Studio, Ollama, or self-hosted models
  • Caching Layer: 3600s TTL, 250 entries max for performance
  • Retry Logic: 2 retries with 0.5s backoff factor
  • Timeout Management: 60s for generation, configurable

Security & Privacy

  • API keys in system keychain: Never stored in plain text
  • Local data storage: Your artifacts stay on your machine
  • No cloud requirements: Run completely offline with local models
  • Configurable endpoints: Route to your infrastructure

Use Cases

Solo Developers

Managing multiple projects? AI Architect organizes documentation across all your initiatives without manual tracking spreadsheets.

Scrum Teams

Keep sprint documentation current. Generate PRDs from user stories and acceptance criteria. Maintain traceability for retrospectives.

Technical Leads

Document architecture decisions, API contracts, and system specifications from scattered design notes and meeting minutes.

Product Managers

Consolidate stakeholder feedback, user research, and feature requests into comprehensive PRDs ready for engineering handoff.

Consultants

Manage documentation for multiple clients. Switch contexts instantly. Generate client-specific documentation with custom instructions.

Enterprise Teams

Maintain compliance with traceability matrices. Generate audit-ready documentation. Support regulatory requirements with validation reports.

Why AI Architect?

Built by Practitioners, For Practitioners

Created by Rod Claar, Certified Scrum Trainer with 30+ years of software development experience. AI Architect embodies lessons learned from decades of watching documentation become a bottleneck.

Test-First AI Development Methodology

Every feature built using Test-First AI principles: understand requirements, validate approach, implement with AI assistance, verify results. The methodology is proven—AI Architect is the proof.

Respects Your Process

Not opinionated about your tech stack, project methodology, or team structure. AI Architect adapts to YOUR workflow, not the other way around.

Transparent AI Operations

No black boxes. Preview every prompt. Understand token usage. Control model selection. See exactly what the AI is doing.

Reliable Fallbacks

LLM unavailable? Automatic fallback to template generation. Your workflow continues regardless of external dependencies.

Local-First Architecture

Your data stays on your machine. Use local models for sensitive projects. No forced cloud dependencies.

Real-World Examples

AREDN Mesh Networking Project

Rod uses AI Architect to document complex amateur radio mesh networking systems. Custom instructions emphasize FCC Part 97 compliance and amateur radio terminology. The comprehensive specifications include sequence diagrams for radio protocol interactions.

Enterprise E-Commerce Platforms

Template-based generation for standardized checkout flows across multiple clients. Consistent documentation structure enables code reuse and accelerates delivery.

Legacy System Modernization

Import scattered Word docs, old Confluence pages, and email threads. AI extracts requirements, identifies gaps, and generates modern documentation for system rewrite.

Regulatory Compliance Projects

Custom instructions for HIPAA, SOC 2, or industry-specific standards. Traceability matrices link requirements through implementation for audit trails.

Getting Started

System Requirements

Operating System

Windows 10+, macOS 10.15+, or Linux

Memory

4GB RAM minimum, 8GB recommended

Storage

500MB for application, additional space for projects

Python

3.8+ (bundled with desktop app)

Network

Optional (required only for cloud-based LLM APIs)

Installation Steps

1

Download AI Architect desktop application for your platform

2

Run installer and follow setup wizard

3

Configure AI endpoint (LM Studio, Ollama, or cloud API)

4

Create your first project and import artifacts

5

Start generating documentation

Learning Resources

Quick Start Guide

Built into the application

Video Tutorials

Step-by-step walkthroughs

Sample Projects

Pre-configured examples to explore

Documentation

Comprehensive user guide and API reference

Support

Community forum and direct coaching options

Pricing & Availability

AI Architect is currently in active development with beta access available through AgileAIDev.com coaching programs.

Beta Access Includes

  • Desktop application for Windows, macOS, and Linux
  • All core features (Import, Analysis, Validation, Generation)
  • Local model support (no recurring API costs)
  • Updates and bug fixes
  • Community forum access
Request Beta Access

Optional Add-Ons

  • AI-Enhanced Scrum Coaching: Learn to integrate AI Architect into your development workflow
  • Custom Prompt Development: Industry-specific prompt engineering
  • Team Training: Workshops for adopting AI-assisted documentation practices
Learn More

Frequently Asked Questions

Q: Does AI Architect require an internet connection?
A: No. With local models (LM Studio, Ollama), AI Architect runs completely offline. Internet is only required for cloud-based LLM APIs.
Q: What happens to my data?
A: All data stays on your local machine. Artifacts, requirements, and generated documents are stored in SQLite databases on your file system. Nothing is sent to external servers unless you use cloud-based LLM APIs.
Q: Can I use my own LLM models?
A: Yes. AI Architect supports any OpenAI-compatible API endpoint. Run models locally or point to your own infrastructure.
Q: What file formats are supported for import?
A: Markdown (.md), JSON, YAML (.yaml, .yml), text files (.txt), PDFs, feature files (.feature), and more. The smart importer uses glob patterns for maximum flexibility.
Q: How long does PRD generation take?
A: Template-based generation: seconds. LLM-based generation: 30-90 seconds depending on project complexity and model speed.
Q: Can I edit the generated documentation?
A: Absolutely. AI Architect generates markdown files you can edit in any text editor. If requirements change, re-ingest artifacts and regenerate.
Q: Does AI Architect integrate with Jira, GitHub, or other tools?
A: Currently, AI Architect operates on exported artifacts (markdown, JSON, etc.). Direct integrations are planned for future releases.
Q: What makes AI Architect different from other documentation tools?
A: Dual-mode generation (template + LLM), complete transparency (prompt preview), local-first architecture, and built-in validation. Plus, it's created by a practitioner who's lived the documentation pain for 30+ years.

About the Creator

Rod Claar, Founder & Principal Consultant at AgileAIDev.com, is a Certified Scrum Trainer with 30+ years of software development experience. He teaches AI-Enhanced Scrum methodology and helps technical teams integrate AI tools while maintaining agile practices.

Rod's career spans retail management, home construction, technical support, custom development, core product development, consulting services, and software training. He's implemented Scrum in enterprise environments, taught TDD and design patterns, and now focuses on helping teams leverage AI effectively.

An amateur radio operator (K7LAP) and AREDN mesh networking enthusiast, Rod combines traditional software engineering rigor with modern AI capabilities. AI Architect is built using the same Test-First AI Development methodology he teaches.

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