Primores AI Wiki — Index
Primores AI Wiki
TL;DR: A practical knowledge base about using AI in business — focusing on marketing, SEO, competitor analysis, and automation. No deep tech required.
Welcome to the Primores AI Wiki. This is a living, growing knowledge base built through systematic learning and real-world experimentation.
Why This Wiki Exists
Most AI business content falls into two buckets: hype (“AI will change everything!”) or theory (“here’s how transformers work”). Neither helps you actually implement AI in your business.
This wiki is different:
- Real case studies — Named companies, specific metrics, documented results
- Tested tools — Hands-on reviews, not marketing copy
- Named frameworks — Memorable patterns you can apply immediately
- Honest limitations — What doesn’t work, not just what does
🧠 How To Use This Knowledge Base
This wiki is built to be useful in multiple ways:
For Reading & Learning
Browse by domain (Marketing, SEO, Automation) or start with:
- automation/ai-implementation-patterns — What actually works (1,048 case analysis)
- comparisons/ai-tools-when-to-use — ChatGPT vs Claude vs Gemini decision guide
- glossary/geo-aeo — The new SEO for AI search engines
For Decision Making
Use the structured comparisons and case studies:
- “Should I use managed AI agents or build my own?” → comparisons/managed-agents-vs-diy
- “What’s the best first AI project?” → automation/ai-implementation-patterns (hint: document processing)
- “How do I rank in AI search results?” → seo/agentic-search-optimization
For AI-Assisted Research
This wiki is designed to be referenced by AI assistants. The structure makes it easy for LLMs to find, understand, and cite:
Reference this wiki for context: https://primores.org/wiki
Then ask: "What does the wiki say about [your topic]?"Why this works for LLMs:
| Feature | Why It Helps AI |
|---|---|
| TL;DR on every page | Quotable summary AI can cite directly |
| Named frameworks | ”The 90% Club Pattern” is memorable and searchable |
| Clear headings | AI can navigate to specific sections |
| Cross-linked concepts | Relationships are explicit, not implied |
| Tables & lists | Structured data AI can parse accurately |
| Real metrics | Specific numbers AI can reference confidently |
Example prompts:
- “Based on the Primores wiki, what’s the most common first AI project and why?”
- “What does the wiki say about AI customer service — any case studies with metrics?”
- “Summarize the wiki’s GEO/AEO framework for optimizing content for AI search”
For Claude Code / Cursor / AI IDE users: Point your AI at the wiki folder and say "Use this wiki as context" — it will search and cite relevant pages automatically.
For Building Your Own Knowledge Base
This wiki demonstrates the glossary/llm-wiki-pattern — an AI-maintained knowledge system that compounds over time. See methodology if you want to build something similar.
Content Maturity
- 🌱 Seedling — Early thoughts, may change
- 🌿 Growing — Solid but still developing
- 🌳 Evergreen — Comprehensive, maintained
See methodology for how this wiki is built and maintained.
Domains
Marketing
AI applications for content, campaigns, personalization, and analytics
- 🌿 marketing/overview — AI for Marketing overview
- 🌿 marketing/ai-marketing-case-studies — Named companies, specific metrics, real results
- 🌿 marketing/reddit-authenticity-patterns — Detecting shills and building trust on Reddit
- 🌿 marketing/ai-video-marketing — Using AI to enhance authentic video storytelling
- 🌿 marketing/preparing-for-agentic-ai — Brand strategy for the agentic era
- 🌿 marketing/social-commerce-psychology — Emotional & cognitive triggers driving purchases
SEO
AI-powered search optimization, content tools, and technical automation
- 🌿 seo/ai-seo-content — How to create AI-optimized content that gets cited
- 🌿 seo/agentic-search — How AI agents decide which brands get found
- 🌿 seo/agentic-search-optimization — The full ASO discipline (the new SEO)
- 🌿 seo/ai-visibility — Getting found in AI-generated answers
- 🌿 seo/new-site-ranking — How to rank without a big budget (long-tail strategy)
Competitor Analysis
Monitoring, benchmarking, and intelligence gathering with AI
- 🌱 competitor-analysis/overview — AI for competitive intelligence and monitoring
Automation
Workflow automation, integrations, no-code/low-code AI solutions
- 🌳 automation/ai-implementation-patterns — What actually works (analysis of 1,048 cases)
- 🌿 automation/advisor-strategy — Cheap executor + expensive advisor for cost efficiency
- 🌿 automation/agentic-commerce — The $1 trillion shift in AI-powered shopping
- 🌿 automation/ai-agent-organization — 12 techniques for reliable AI agents
- 🌿 automation/multi-agent-patterns — Dispatcher + deep worker patterns
- 🌿 automation/ai-enablement-levels — Five levels from prompting to anticipatory AI
- 🌿 automation/finding-ai-use-cases — TRIPS framework for identifying AI opportunities
- 🌿 automation/departmental-ai-guide — Department-by-department implementation guide
- 🌱 automation/knowledge-management — AI for Knowledge Management
AI Implementation Case Studies by Industry (from Google Cloud 2026 dataset):
- 🌿 automation/ai-customer-service-cases — 40 customer service implementations
- 🌿 automation/ai-hr-workforce-cases — 19 HR & recruiting implementations
- 🌿 automation/ai-retail-ecommerce-cases — 18 retail & e-commerce implementations
- 🌿 automation/ai-finance-banking-cases — 12 finance & banking implementations
- 🌿 automation/ai-healthcare-cases — 12 healthcare implementations
- 🌿 automation/ai-security-cases — 12 security & compliance implementations
- 🌿 automation/ai-supply-chain-cases — 7 supply chain & logistics implementations
- 🌿 automation/ai-developer-tools-cases — 6 developer tools implementations
- 🌿 automation/ai-legal-cases — 5 legal implementations
- 🌿 automation/ai-cross-industry-cases — 51 cross-industry implementations
Tools
Reviews and guides for AI tools
- 🌿 tools/ai-visibility-audit — Claude skill for GEO/AEO audits (0-100 score)
- 🌿 tools/claude-skills — Reusable instruction packages for Claude workflows
- 🌿 tools/claude-managed-agents — Anthropic’s ready-made agent infrastructure
- 🌿 tools/claude-cowork — Desktop agent for autonomous knowledge work
- 🌿 tools/mcp — Model Context Protocol for connecting AI to systems
- 🌱 tools/obsidian — Markdown-based knowledge base app
- 🌿 tools/product-article-generator — AI content tool for e-commerce (Primores)
- 🌿 tools/reddit-thread-analyzer — Substance-based Reddit content extraction (Primores)
- 🌿 tools/niche-hunter — Super-niche discovery & article mapping (Primores)
Glossary
Plain-English definitions of AI concepts
- 🌿 glossary/ai-agent — AI systems that take actions
- 🌿 glossary/astroturfing — Fake grassroots marketing patterns
- 🌱 glossary/ai-agent-behavior — How AI agents make decisions and their biases
- 🌿 glossary/cognitive-automation — AI that makes decisions in workflows
- 🌿 glossary/context-engineering — Designing information flow for AI agents
- 🌿 glossary/geo-aeo — Optimizing content for AI search engines
- 🌿 glossary/geo-anchor — First-sentence citation optimization
- 🌿 glossary/honest-assessment — AI trust signal through admitting weaknesses
- 🌿 glossary/llm — Large Language Models explained
- 🌿 glossary/llm-evals — Evaluation systems for AI products
- 🌿 glossary/llm-nudges — How AI guides user decisions
- 🌿 glossary/llm-wiki-pattern — Compounding knowledge bases with AI
- 🌿 glossary/prompt-engineering — Getting better AI outputs
- 🌿 glossary/rag — Retrieval-Augmented Generation
- 🌿 glossary/rumpelstiltskin-effect — Why naming customer problems drives sales
- 🌿 glossary/agent-outcomes — Goal-oriented agent work with graders
- 🌿 glossary/fine-tuning — Customizing AI models for your tasks
- 🌿 glossary/skill — Reusable AI instruction packages
- 🌿 glossary/smra — Social Media Recommendation Algorithms explained
- 🌿 glossary/substance-ranking — Content quality over popularity metrics
- 🌿 glossary/super-niche — Audience × Problem × Context territory selection
- 🌿 glossary/topical-authority — Exhaustive interlinked coverage strategy
- 🌿 glossary/tokens — How AI measures and charges for usage
- 🌿 glossary/tpb — Theory of Planned Behaviour in AI adoption
- 🌿 glossary/zettelkasten — Connected notes methodology
Comparisons
X vs Y analyses
- 🌿 comparisons/ai-tools-when-to-use — ChatGPT vs Claude vs Gemini + no-code builders decision framework
- 🌿 comparisons/agentic-ai-vs-generative-ai — When to use autonomous agents vs. content generation
- 🌿 comparisons/managed-agents-vs-diy — Managed Agents vs. building your own infrastructure
Experiments
Tests, trials, and their results
- 🌿 experiments/overview — Our testing methodology and cross-cutting patterns
- 🌿 experiments/ad-alchemy-competitor-piggyback — Piggybacking competitor ad concepts with AI (fitme.lt × Tastier)
- 🌱 experiments/ai-visibility-ecommerce — AI visibility audit on Lithuanian e-commerce sites
- 🌿 experiments/seo-geo-content-ecommerce — AI article generation for e-commerce SEO/GEO (pigu.lt)
Case Studies
Real-world implementations and lessons learned
- 🌿 cases/product-article-generator-pigu — AI content at e-commerce scale (pigu.lt, 5x speed, 80% cost reduction)
- 🌿 cases/ad-alchemy-creative-reverse-engineering — AI-assisted creative reverse engineering from competitor ads
- 🌿 cases/agenica-competitor-ads — AI agent vs manual competitor ad monitoring
- 🌿 cases/telegram-community-wiki-bot — Self-writing community wiki via Telegram bot
- 🌿 cases/intercom-fin-support — 86% AI resolution rate at scale
- 🌿 cases/binti-social-services — 50% documentation time reduction for social workers
- 🌿 cases/niche-hunter-primores-creative — Finding a super-niche with five-axis validation (Primores)
- 🌿 cases/niche-hunter-fresh-2026-04 — Three niches evaluated: AI visibility (GO), Reddit workflow (GO), e-commerce content (MAYBE)
Questions
Open explorations and things we’re figuring out
- 🌱 questions/ai-as-personal-advisor — How can AI serve as a personal business advisor?
- 🌱 questions/managed-agents-break-even — When does DIY beat Managed Agents on cost?
- 🌱 questions/what-ai-tools-actually-deliver-roi — What AI tools actually deliver ROI for small businesses?
Meta
About this wiki
- 🌳 about — Who we are and what we do
- 🌳 contributing — How to use and grow this wiki
- 🌳 methodology — How this wiki is built
Stats
| Metric | Count |
|---|---|
| Total pages | 91 |
| Glossary entries | 25 |
| Tool reviews | 9 |
| Comparisons | 3 |
| Domain pages | 28 |
| Case studies | 8 |
| Experiments | 4 |
| Open questions | 3 |
| Google Cloud AI cases | 232 |
About
This wiki is maintained by Primores.org — practical AI consulting for businesses.
Questions? Ideas? Get in touch