Instead of asking for a generic "review," which can feel transactional, reframe the request. Ask past customers to provide a "reference" for your "digital resume" or "online presence." This reframing highlights the personal impact on your business, making clients more willing to contribute.
LLMs are extremely sensitive to inconsistencies in business data across online platforms. Even minor variations in your Name, Address, and Phone (NAP) can confuse the AI, causing it to drop your business from its recommendations entirely. Strict data consistency is paramount.
Businesses excelling at traditional SEO can still be invisible to AI-powered search engines. AI prioritizes structured data (schema) and directory signals differently than Google's algorithm. A separate strategy for "Answer Engine Optimization" (AEO) is now required.
The common advice to post on Reddit for AI search visibility is often ineffective. Instead, analyze the citations an LLM provides for relevant queries. If the AI isn't sourcing from Reddit, spending time there is a waste. Focus on getting listed where the AI is already looking.
Tools like Claude Code are democratizing software development. Product managers without a coding background can use these AI assistants to work in the terminal, manage databases, and deploy apps. This accelerates prototyping and deepens technical understanding, improving collaboration with engineers.
Contrary to modern SEO advice, web directories are experiencing a renaissance. AI models like ChatGPT use them as primary data sources for local business recommendations, making a presence on platforms like Yelp and WalletHub critical for being found in AI-powered search.
A holistic strategy for AI search optimization (AEO) requires three pillars: presence in key directories (off-page), traditional content optimization (on-page), and structured data via schema.org markup (technical) to ensure the AI can read and understand your services.