Why AI Search on LLMs Matters
Search is changing. Gone are the days when Google alone ruled clicks. Today, people fire natural questions into Large Language Models (LLMs) like ChatGPT, Gemini and Claude. They want concise, authoritative answers. That means your brand needs to show up where these models pull data from.
LLMs are trained on massive datasets. They synthesise info into simple replies. If you’ve ever asked “What’s the best running shoe for a newbie?”, you’ve seen it. A quick, chatty reply that often cites a handful of sources. Those sources are your chance to land a mention—and a click.
Small to medium enterprises (SMEs) often miss out. Traditional SEO tools focus on link juice and keyword density. They skip entity-level tuning. That’s where entity research strategies come in. Think of entities as the nuggets LLMs chew on: names, places, dates, product specs, stats. Get those right, and the model is more likely to spotlight your content.
What Does Brand Visibility on LLMs Look Like?
In a traditional search, you aim for page one. On an LLM, you want to be in the answer. That reply could mention your brand name, quote a fact from your site, or link directly to a product page.
Key signals LLMs use:
- Entities: Named things—people, products, locations.
- Stats & quotes: Numbers and expert lines.
- Citations: Links from credible sites.
- Context: How well your content matches the user’s intent.
Optimising for these signals involves a blend of classic SEO and fresh entity research strategies. You’re not just targeting keywords anymore. You’re mapping concepts, facts and relationships.
Mastering Entities: The Foundation of AI Search
Entities are the DNA of AI search. When an LLM sees “buy 3 tickets to New York”, it breaks down:
- “3” → quantity entity
- “New York” → location entity
Now imagine your product pages and blog posts peppered with relevant entities:
- Technical specs (RAM, screen size, model number)
- Dates & events (launch dates, anniversary sales)
- People & organisations (founder names, partner brands)
Here’s where entity research strategies pay off:
- Use an entity analyser (e.g. Google’s Natural Language API).
- Extract top entities from competitor pages.
- Map out a content plan around gaps.
By tracking entities that your rivals own, you can plan content that fills in missing facts. That boosts your chance of an LLM nod.
Goodie vs. Open-Source AI Visibility Tracking
You might’ve heard of Goodie. It’s a polished platform for AI visibility monitoring. It tracks brand mentions and sentiment across multiple LLMs. It offers dashboards and alerts. Nice.
But it has trade-offs:
- Cost: Enterprise pricing.
- Black box: Limited customisation.
- Scope: Focus on established markets, not SMEs.
Our project, AI Visibility Tracking for Small Businesses, takes a different tack:
- Fully open-source.
- Affordable—no hidden fees.
- Community-driven.
- Transparent roadmaps and code.
We give you the same real-time insights Goodie promises, except you can inspect every line of code. You decide how deep to dive. You own your data.
Building Your Open-Source Entity Workflow with Maggie’s AutoBlog
Ready to dive in? Here’s a straightforward, step-by-step process you can follow today. We leverage Maggie’s AutoBlog—our high-priority tool—to automate content generation and import key entities.
- Install the open-source visibility tracker from Geovote’s GitHub.
- Run an audit on your top 10 pages. Extract entities using a free API.
- Feed those entities into Maggie’s AutoBlog.
- Generate blog drafts with embedded facts:
– Product specs
– Case study snippets
– Local references (great for geo-targeted search) - Review drafts, add quotes or stats for credibility.
- Publish and monitor your visibility dashboard in real time.
This process brings entity research strategies front and centre. You’re not guessing. You’re following data. And because Maggie’s AutoBlog auto-formats HTML, you spend less time on code and more on ideas.
PR and Content Tweaks to Boost LLM Citations
Good content still rules. But on LLMs, extra care goes to:
- Press releases: Embed target entities in your headlines and first paragraph.
- Guest posts: Include stats and link back to your site.
- User-generated content: Encourage reviews with product specs.
LLMs convert words into tokens. They then map tokens into a vector space. If your tokens align with popular queries, you get more visibility. That’s a fancy way of saying: choose your words and numbers wisely.
Bullet-proof your content by:
- Using quotes from recognised experts.
- Citing credible sources (academic papers, industry reports).
- Highlighting technical terms your audience cares about.
These minor tweaks can make your brand the most frequently cited in an LLM response.
Measuring Your Success
Data is your friend. Track:
- Entity mention frequency: How often is “Model X” cited?
- Visibility share: Compare your mentions vs competitors.
- Traffic lift: Are more users clicking through?
Our open-source tool outputs CSVs you can plot in any spreadsheet. No extra licences. No locked-in modules.
If you want a quick overview, Maggie’s AutoBlog dashboard gives you a snapshot of:
- New entity terms discovered.
- Content published this week.
- Visibility trend lines.
Pretty neat. And all transparent.
Common Pitfalls and How to Avoid Them
Even seasoned pros trip up. Watch out for:
- Over-stuffing entities. It looks spammy.
- Ignoring context. Entities need to fit naturally.
- Forgetting to update stale facts. LLMs love fresh data.
Stick to your entity research strategies. Audit regularly. Update content. Stay relevant.
Wrapping Up and Next Steps
Optimising for AI search on LLMs doesn’t have to cost the earth. With open-source AI visibility tracking, Maggie’s AutoBlog, and a solid grasp on entity research strategies, you can compete with big players—without breaking the bank.
It’s time to shift from guesswork to data-driven decision-making. Embrace transparency. Harness community contributions. And watch your brand climb into those AI replies.
Share
Related posts
