Where models conduct initial research

Search engines return ranked lists from their pre-built index. AI models conduct research in real-time. When someone asks "What are good alternatives to [incumbent]?", the model doesn't have a prepared answer. It searches the web during the conversation, evaluating sources and synthesizing information.

The typical research path includes category directories. G2, Capterra, and ProductHunt are high-signal sources because they aggregate user reviews and structure product information consistently. Models check these first for landscape understanding and credibility signals.

Community discussions provide contextual richness. Reddit threads, Hacker News comments, and industry Slack channels give models insight into how real users talk about products. These sources carry weight because they're candid and unfiltered. A genuine recommendation in a Reddit thread about "tools for immigration lawyers using Microsoft Word" can be the difference between invisibility and inclusion.

Comparison articles and roundup posts help models understand positioning. "Best X for Y" articles, head-to-head comparisons, and alternative lists provide structure that models can parse easily. These pieces explicitly answer positioning questions: who's this for, how does it compare, what are the tradeoffs?

Niche industry sites and publications matter for vertical products. If you build for legal professionals, models will check legal tech blogs. For developer tools, they'll scan developer-focused publications and forums. The model is trying to understand where you fit in the specific context the buyer cares about.

Long-tail earned media beats long-tail keywords

SEO taught us to optimize for keyword variations. "Best project management software," "project management tools for startups," "free project management apps." You'd create pages targeting each variation to capture different search intents.

AI discovery requires presence, not optimization. You need to be mentioned in scattered contexts across many sources. A single mention in a credible forum thread about "tools for remote teams managing client projects" might reach three people organically but influence thousands through AI recommendations. The model reads that thread when researching for a buyer with similar constraints.

This is fundamentally different from keyword strategy. You're not trying to rank for specific searches. You're trying to exist in the web of sources that models consult when researching your category and use case. Breadth matters more than depth. Better to have light mentions across twenty relevant sources than heavy optimization of three owned pages.

The mechanism resembles PR more than SEO. You're earning mentions and building presence in places your audience already trusts. The difference: your real audience is the AI model first, the human reader second. The model aggregates these scattered signals into a coherent understanding of whether you exist and where you fit.

The systematic presence strategy

You can't manufacture brand awareness overnight, but you can systematically place yourself where models look.

Claim and optimize your directory listings. G2, Capterra, ProductHunt, and category-specific directories all matter. Models check these regularly. An empty or outdated listing signals abandonment. A complete profile with recent reviews and detailed feature lists signals active product. Take the time to fill these out thoroughly. Use consistent language about your positioning across all directories. Make sure your feature descriptions are literal and specific, not marketing fluff.

Contribute genuinely to community discussions. Reddit and Hacker News are allergic to spam, but they reward helpful participation. Answer questions in your domain. Share insights from your work. When relevant and appropriate, mention your tool—but only when it actually solves the problem someone described. A handful of genuine, helpful comments will do more for discovery than dozens of promotional posts.

Get featured in comparison content. Reach out to bloggers who write "best of" lists in your category. Offer to be included, provide accurate information about your product, and make it easy for them to write about you. Most comparison articles exist because they drive traffic; authors want comprehensive coverage. Be easy to include.

Write guest posts for niche industry publications. If you serve a vertical market, write for the publications that market reads. This isn't about backlinks—it's about presence in the sources models consult when researching that industry. A single article in a legal tech blog might get mentioned by models dozens of times when buyers ask about legal software.

Participate in ProductHunt launches and alternatives lists. These create durable presence. A ProductHunt page with engagement and reviews stays live indefinitely. Models reference these pages frequently when mapping categories and alternatives.

Show up in communities where your users gather. Industry Slack channels, LinkedIn groups, specialized forums. Participate genuinely, help people solve problems, and establish presence. Models increasingly crawl these sources for current information and authentic user sentiment.

Why this differs from link building

This isn't about domain authority or link juice. It's about breadth of corroborating mentions. SEO link building optimized for powerful backlinks from high-authority sites. The goal was to pass ranking signals to your pages.

AI discovery optimizes for scattered presence across credible sources. The model that finds you mentioned in five different contexts starts to build confidence you're real. Found in fifteen? You're established. Twenty? You're a known entity in your category.

The quality bar is "credible and relevant," not "high domain authority." A mention in a focused subreddit with 5,000 subscribers can matter more than a link from a general news site with millions of visitors. The model is evaluating relevance to the buyer's specific context, not aggregating authority scores.

This means tactics that were lower-value in SEO become essential in AI discovery. The niche forum post. The small but engaged Slack community. The industry newsletter with 2,000 subscribers. Each is a node in the research graph. Models follow context, not just authority.

Different challenge for enterprises

Large companies don't have a discovery problem—they have a filtering problem. Models know you exist but exclude you based on assumptions: too expensive, overkill for smaller teams, legacy technology, complex implementation. That's a different challenge requiring different content, but the same underlying principle applies: specific evidence placed where models look.

For enterprises, the equivalent of "long-tail earned media" is "context-specific positioning." You need mentions that say "Company X works well for [specific scenario that's smaller than your full enterprise positioning]." The mid-market case study. The startup-friendly tier. The specific use case that doesn't require your full platform. These mentions help models include you in more conversations instead of filtering you out by default.

First presence, then preference

Most AEO tools measure whether you're mentioned in initial responses to broad prompts. That's a useful metric, but it's insufficient. Discovery gets you into the conversation. Opinion work—the detailed content about what you're good at, who you're for, and why someone should choose you—determines whether you get recommended after the buyer adds constraints.

Both matter. If models don't know you exist, they can't recommend you. If they know you exist but have no informed opinion about your strengths, they'll default to safer, more established options. The directory listings and forum mentions solve discovery. The detailed, proof-rich content on your own properties solves preference.

The startup journey typically follows this sequence: first, earn scattered mentions that make you visible. Second, consolidate those mentions with your own authoritative content. Third, build enough corroborating proof that models recommend you confidently in your specific contexts. Each stage builds on the previous one. You can't skip discovery and jump to preference.

The good news: this work compounds. Each legitimate mention increases the probability models will find you. Each time they find you, they strengthen their understanding of your positioning. Over time, you move from "I've never heard of them" to "I know what they do" to "I know who they're good for." That progression is how unknowns become recommendations.