Recent data shows that LLM traffic converts nearly nine times better than traditional search. Some SaaS sites are already seeing around 1% of their sessions come from LLMs, as classic search traffic declines. By 2026, traditional search engine volume is expected to decline by 25%, with search marketing losing market share to AI chatbots and virtual agents.
To show up in AI answers, you need more than rank tracking. You need to know which keywords surface in AI Overviews, ChatGPT, Perplexity, and Copilot, and which pages each cites. That is exactly what Tesseract for LLM reveals.
Think of Tesseract LLM as the bridge between interesting AI visibility data and a prioritized SEO to-do list your team can act on this week. Once you insert the keywords and URLs you care about, the platform shows where you appear, where you do not, and what to fix next. Now, let us learn how to put it to work.
What Tesseract for LLM Actually Measures
It shows which of your keywords surface answers across Google AI Overviews, ChatGPT, Perplexity, and Copilot, and whether your domain is visible in those answers. Here is an overview of what it actually offers:
1. See Exact Pages Cited by Various LLMs
In LLM contexts, it lists the specific pages cited for each query so you know what content AI trusts and what it skips.
2. Spot Low-ranking Pages and Fix Them
It flags pages that rank lower for target terms and suggests SEO changes to improve inclusion.
3. Start With Your Inputs
You must add your priority keywords and URLs. Once entered, Tesseract begins analyzing visibility and citations across AI platforms.
4. Understand AI Content Prioritization
Tesseract for LLM highlights traits common to cited pages: concise summaries, structured answers, authoritative references, and consistent internal linking.
5. Optimize for AI First Search
Turn those traits into repeatable page patterns. Place direct answers up top, add supporting evidence, apply relevant schema, and connect related pages. Then measure whether inclusion and citations rise.
6. Compare AI Search Visibility
Stack your visibility against competitors across Google AI Overviews, ChatGPT, and Perplexity to find topics where they are featured and you are not.
7. Monitor Competitor Mentions in AI Search
See how AI models describe and reference competing brands for your keywords. If rivals are cited for definitions, specifications, or how-tos, build clearer, more complete answers on those angles.
8. Gain an AI Search Advantage
Use the data to prioritize moves with the highest likelihood of inclusion. Shape your pages to match what AI prefers, so your entries appear and remain visible.
9. Track Your Rankings in AI Search
Unlike traditional rank trackers, Tesseract for LLM shows where your keywords appear inside AI answers and which pages earn citations. Page-level suggestions help improve clarity, structure, and supporting signals to outperform competing sources.
10. Get a Precise, Page-tied Map
This is not a vague score. It is a keyword-by-keyword map of performance across AI platforms, tied directly to your pages so that you can turn insights into action quickly.
From Insight to Execution: A Practical Playbook
You have the signals. Now turn them into wins. Tesseract for LLM highlights where your keywords appear in AI-generated answers and which pages are cited. Use this simple sequence to convert those insights into page upgrades that earn inclusion and hold it.
1. Set Targets
Add priority keywords and URLs. Group by intent and product line for sharper analysis.
2. Read Rankings
See which keywords trigger AI answers across platforms, where you’re cited, which pages get credit, and where competitors lead.
3. Find Gaps
Flag queries with AI answers that exclude your brand and note the competitors cited.
4. Select Pages
Map each gap to the single most relevant page to improve. Avoid spreading edits thin.
5. Apply Suggestions
Use Tesseract LLM’s page-level guidance to refine headings, craft concise summaries, add structured FAQs, improve schema, and strengthen internal links.
6. Ship and Monitor
Publish, then track inclusion, citation frequency, and placement for the same keyword-page cohort. If progress stalls, consider deepening summaries, enhancing the schema, or adding clarifying visuals/tables.
7. Scale and Refresh
Roll winning patterns to the full cluster. Update keyword and URL sets as products evolve and keep tracking cohorts to maintain trend lines.
Turn AI Insights into Rankings
The path forward is simple. Configure your project by inserting your priority keywords and URLs. Let Tesseract for LLM reveal where you appear across AI Overviews, ChatGPT, Perplexity, and Copilot, which pages earn citations, and which pages need work. Use the platform’s page-level suggestions to craft concise briefs, deliver targeted updates, and observe an increase in inclusion and citations.
Revisit your Tesseract LLM snapshots, powered by AdLift, during weekly and monthly reviews, prioritize the biggest near-wins, and scale winning patterns across each keyword cluster. If you are ready to convert AI visibility into measurable growth, insert your keywords into Tesseract, update your first few pages, and review results. Then repeat, expand, and keep climbing where customers actually find answers.
