What This Wiki Is

The AI Solutions Wiki is a practical knowledge base for teams building AI-powered products and workflows. The focus is on applied AI - not theoretical research, not vendor marketing, but the real patterns, tools, and decisions that come up when you are turning an idea into a working system.

Every article here is written to be immediately useful. Definitions include context about when and why you would use something, not just what it is. Guides include real trade-offs. Comparisons are honest about where each option falls short.

Who It Is For

This wiki is useful at two career stages:

Practitioners who are new to AI - engineers, product managers, and architects who understand software but are mapping a new terrain. The glossary, guides, and frameworks sections give you the vocabulary and structure to navigate AI decisions confidently.

Experienced AI builders - people who have shipped AI systems and want a reference for patterns, tools, and comparisons without re-reading vendor documentation. The patterns, case-patterns, and comparisons sections are aimed at you.

The content is also useful for anyone who needs to explain AI decisions to stakeholders - the frameworks and comparisons are written to support clear communication, not just technical implementation.

How the Content Is Organized

Guides - step-by-step implementations, from getting a first Bedrock prototype running to designing a multi-agent architecture.

Solutions - industry-specific AI applications covering finance, insurance, media, logistics, healthcare, and more. Each solution article describes the problem, the architecture, the tools, and the operational considerations.

Patterns - reusable technical patterns that appear across many different AI projects: RAG, agent orchestration, data pipelines, prompt design.

Case Patterns - real-world deployment patterns drawn from production use cases. Anonymized, with details focused on what makes the architecture work.

Tools - practical coverage of AI platforms and frameworks: what they do well, what they do not, pricing, and when to use them.

Comparisons - side-by-side analysis for common decision points: Bedrock vs Azure OpenAI, RAG vs fine-tuning, Claude vs GPT.

Frameworks - structured thinking tools for AI project planning: use-case scoring, readiness assessment, workshop methodology.

Glossary - plain-English definitions for AI, ML, and cloud terms.

Ideas - short concept pieces for automation use cases worth exploring.

How the Content Is Maintained

Articles are reviewed and updated as tools evolve and new patterns emerge. Pricing information and model capabilities change frequently - always verify current pricing at the provider’s official documentation before making architecture decisions based on figures quoted here.

If you find an error or a gap, the best way to contribute is through the GitHub repository at github.com/mzzavaa/ai-solutions-wiki.