7 Steps in the Annapurna Influence AI Visibility Strategy

If a buyer asks ChatGPT for the best option in your category and your brand is missing from the answer, nothing in your analytics will tell you. No impression gets logged. No bounce gets recorded. The buyer just never hears your name.

That is the problem we work on at Annapurna Influence. In this post I want to lay out our full AI visibility strategy, step by step, with the research and the numbers behind each choice. I would rather you judge the method than take our word for it.

The short version: our AI visibility strategy is a seven-step loop. We research the questions buyers ask, track how every answer engine responds, map the sources behind those answers, reach out to those sources, audit your website, write content for your site, and publish content across third-party publishers. Then we measure and repeat.

Why AI visibility needs its own strategy

Three numbers shaped how I think about this.

• 1.2%. SOCi's 2026 Local Visibility Index looked at roughly 350,000 business locations. Google's local 3-pack surfaced a location 35.9% of the time. ChatGPT recommended 1.2% of them, Perplexity 7.4%, and Gemini 11% (Search Engine Land).

• 26%. Ahrefs studied 75,000 brands and found that about one in four had zero mentions in Google AI Overviews (Ahrefs).

• 40%. The founding academic paper on this topic, led by researchers at Princeton University and IIT Delhi and presented at KDD 2024, tested nine content methods across 10,000 queries. The authors reported that "GEO can boost visibility by up to 40% in generative engine responses" (Aggarwal et al., KDD 2024).

So the gap is large, and it can be closed. That combination is the reason our company exists.

Five terms I will use in this post

1. Query research: we start with the buyer's question

Most AI visibility projects go wrong at the first step. A tool generates 300 prompts in ten seconds, and the dashboard looks impressive, but half of those prompts are questions no real customer would ask.

We do it the slow way. Our researchers first learn the business: what it sells, who buys it, where it operates, and which alternatives a buyer would weigh. Then we score possible prompts on five signals: search volume, keyword difficulty, search intent, geographic relevance, and business relevance.

We sort every prompt into one of three types, which I think of as stages of awareness:

1. Brand-aware. The buyer already knows you. Example: "Tell me about Acme Plumbing."

2. Solution-seeking. The buyer knows what they need but not who to hire. Example: "Best emergency plumber in Round Rock."

3. Problem-aware. The buyer only knows something is wrong. Example: "Why is my water heater making a popping sound?"

People also phrase things differently for AI. Someone types "bounce house rental fargo" into Google, then asks an assistant, "Where can I take my 7-year-old for a birthday party this weekend?" Same intent, very different wording. AirOps reviewed more than 245,000 prompts that marketing teams track and found they peak at 6 to 7 words, while real ChatGPT prompts run much longer (AirOps).

Our rule here is simple. As we wrote in our own methodology post, "relevance is more valuable than volume" (how we select prompts). Every plan starts with at least 20 researched prompts, and you can add your own.

2. Prompt tracking: we score every answer, on every engine

Once the prompt set is final, we run those prompts continuously across each major answer engine. Depending on the plan, that covers three, five, or seven engines: ChatGPT, Gemini, Perplexity, Claude, Grok, Kimi, and DeepSeek.

Tracking per engine matters because the engines disagree. In the SOCi data above, Gemini recommended local businesses about nine times as often as ChatGPT did. A brand can look healthy on one platform and be absent on another.

Each answer is read by a judge service that fills out nine fields, including position, sentiment, accuracy, depth, and whether a source was cited. Two parts of the scoring are worth explaining:

• Position is weighted as 100 ÷ position. Being named first is worth 100 points. Second is worth 50. Fifth is worth 20. The curve is steep on purpose, because readers pay the most attention to the first name in a list.

• Harder questions count more. A brand-aware prompt has a weight of 1.00, a narrowed-problem prompt 1.25, and a brand-unresolved problem prompt 1.50. If the AI volunteers your brand when nobody asked for it by name, that mention is worth 50% more.

Here is the worked example from our scoring system post. A brand runs 3 prompts on 2 engines, which gives 6 evaluations. It gets mentioned in 4 of them. The mention rate is 4 ÷ 6, or 66.67%. The visibility score, which averages the position-weighted scores, comes out to 43.06 out of 100.

There is also a step called entity disambiguation, which simply means checking that the name in the answer is really you. If the AI mentions Annapurna Labs (Amazon's chip unit) or Mount Annapurna, that is not us, and the judge throws it out. Low-confidence matches count as zero so the score never gets inflated.

3. Citation mapping: we find the pages the AI trusts

I consider this the most important step in the whole strategy. Answer engines do not make up their recommendations from nothing. Each answer is assembled from a short list of pages, directories, and forums that the retrieval layer pulled in. If we know which pages those are, we know where your brand needs to appear.

So for every tracked prompt, on every engine, we record the sources cited. We also check your brand's presence across the directories and listing services in your category.

The research on how engines pick sources explains why this takes real effort:

• In 89.6% of searches, ChatGPT expanded the original prompt into two or more fan-out queries before choosing what to cite.

• 32.9% of cited pages were found only through those fan-out queries, never through the original prompt.

• Only 15% of the pages ChatGPT retrieves end up cited.

• 84% of all citations come from outside the top 100 domains, and the single most-cited domain, Reddit, holds just 2.36% of citations.

All four figures come from AirOps Research, which sums it up in one line: "There is no 'page one' to win." Google describes the same fan-out behavior in its own AI features documentation.

For a small brand, I read that as good news. The citation pool is wide, and a niche blog or a local directory can carry as much weight as a famous publication.

4. Citation source outreach: we ask to be included

Mapping tells us where the AI looks. Outreach gets you onto those pages.

We contact the owners of the cited sources, whether they run a publication, a blog, a directory, or a forum, and request an update that includes your brand. Our plans cover 20, 40, or 100 outreach contacts per month. To be clear about the limits: we make the request, and the site owner makes the decision. Nobody can guarantee a placement.

Why spend effort on other people's websites? Because that is where the strongest signal sits. In the Ahrefs study of 75,000 brands, branded web mentions had a correlation of 0.664 with AI Overview visibility, while backlinks came in at 0.218. A correlation score runs from 0 to 1, and the closer to 1, the more closely two things move together. Mentions tracked with visibility about three times as strongly as links did.

The gap between leaders and everyone else is steep too. A typical brand in the top quarter for web mentions earned 169 AI Overview mentions, against 14 for the next quarter down.

Ryan Law, Director of Content Marketing at Ahrefs, explains the mechanism in that study: "LLMs derive their understanding of a brand's authority from words on the page." The AI learns who you are from what the web says about you, far more than from who links to you.

5. AI visibility audit: we fix how your site reads to a machine

Next we evaluate your website and wider brand presence the way a retrieval system would, then hand you a prioritized list of fixes.

I want to be careful about hype here, because a lot of it gets sold in this category. Google's position is plain: "There are no additional requirements to appear in AI Overviews or AI Mode" (Google Search Central). The same page says you do not need special AI text files or special schema markup.

So our audit focuses on the unglamorous basics that decide whether a page can be retrieved at all:

• Crawler access. We check that your robots.txt file and your CDN are not blocking AI crawlers. OpenAI, for example, uses GPTBot for training and OAI-SearchBot for ChatGPT search, and blocking the second one removes you from search answers.

• Text you can read without JavaScript. If key content only appears after scripts run, some crawlers never see it.

• A clear answer near the top. Pages should state what you do, for whom, and where, in the first few lines.

• Structured data that matches the visible page. We use schema types such as FAQPage and HowTo to make meaning clearer, not as a trick.

• Consistent listings. Name, address, phone number, and category should match everywhere. Listing corrections tend to show up fastest, which is why they are usually our first wins.

6. Content for your website: written by people, built to be cited

Each month we write 10, 20, or 40 articles for your own site, depending on the plan. Human writers produce every piece, and you review it before it ships. Automation handles monitoring and scoring. Research and writing stay with people.

The structure of those articles follows the GEO paper closely. The researchers scored each method using position-adjusted word count, which measures how much of the AI's answer comes from your page and how early it appears. Their results:

Quotations, statistics, and cited sources all landed well above the baseline. Keyword stuffing, the oldest SEO habit there is, did worse than changing nothing.

Here is what that looks like in a single sentence.

• Before: "Many homeowners wait too long to fix a roof."

• After: "Six in ten homeowners delay roof repairs past the point where a patch would work, according to [a named survey with a link]."

The second version gives a language model a fact it can lift and attribute. The first gives it nothing.

Titles matter as well. AirOps found that pages whose titles closely matched the query were cited 20.1% of the time, compared with 9.3% for pages with little overlap. That is why we write a page for one specific question instead of one broad page for a whole category.

7. Content across publishers: we put your brand where engines already look

A blog post on your own domain is one source. Engines want agreement across several. So the last step is distribution: we generate and place content about your brand on directories, publications, and other publisher sites through our partner network.

This is where smaller brands gain the most. In the GEO paper, the Cite Sources method raised visibility by 115.1% for sites ranked fifth in search results, while the top-ranked site lost 30.3% on average. The authors' explanation is that generative engines do not lean on backlinks the way classic search does, so a newer brand has a real opening.

We also keep content fresh. Engines re-crawl, competitors publish, and answers shift from month to month. Our tracking data decides what gets refreshed next. Our About page puts the relationship between the two halves of our work this way: "The tracker measures. The content moves the number." (About Annapurna Influence)

What it costs and how long it takes

Plans are month to month, and you keep every article we write. Full details are on our pricing section.

On timing, most brands see measurable movement within 1 to 6 months:

• Month 1: baseline. Prompts are tracked, sources are mapped, and the audit is delivered.

• Months 2 to 3: new content and completed outreach start showing up in citations.

• Months 4 to 6: coverage compounds, because each citation makes the next one more likely.

For a real example, our published case study with Bounce House Party reports a 157% increase in AI visibility in 3 months (case study).

What I will not promise

Instant results. Published content has to be crawled, indexed, and absorbed by each engine's retrieval layer. No one can switch that on overnight.

A number we fully control. A 2025 benchmark called C-SEO Bench tested content tactics with many competitors using them at once. It found that most of them lose their effect as adoption spreads, and that simply ranking well in the underlying search index did more (Puerto et al.). I take that seriously. It is the reason our strategy pairs content with source outreach, technical fixes, and distribution instead of relying on writing style alone.

Cause and effect from a correlation. The Ahrefs numbers show which signals travel with AI visibility. They do not prove that one causes the other, and the authors say so themselves.

Where to start

If you want to see your baseline before talking to anyone, we run a free report: 10 real prompts about your brand across ChatGPT, Claude, Gemini, and Perplexity, scored and sent back in about 5 minutes. You can request it on our free AI visibility report page.

The seven steps above are the whole strategy. Find the questions, watch the answers, map the sources, earn a place in them, fix your site, write content worth citing, and spread it across the web. Then measure it again next month.

Sources

1. Annapurna Influence. (n.d.). Homepage: Process and pricing. Retrieved October 12, 2026, from https://www.annapurnainfluence.ai/
2. Annapurna Influence. (n.d.). How the Annapurna Influence scoring system works. Retrieved October 12, 2026, from https://www.annapurnainfluence.ai/how-the-annapurna-influence-scoring-system-works
3. Annapurna Influence. (n.d.). How Annapurna selects prompts for tracking and data generation. Retrieved October 12, 2026, from https://www.annapurnainfluence.ai/how-annapurna-selects-prompts-for-tracking-and-data-generation
4. Annapurna Influence. (n.d.). About Annapurna Influence. Retrieved October 12, 2026, from https://www.annapurnainfluence.ai/about
5. Annapurna Influence. (n.d.). Case study: Bounce House Party. Retrieved October 12, 2026, from https://www.annapurnainfluence.ai/case-studies/1
6. Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative engine optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ‘24). https://arxiv.org/abs/2311.09735
7. Puerto, H., Gubri, M., Green, T., Oh, S. J., & Yun, S. (2025). C-SEO Bench: Does conversational SEO work? arXiv. https://arxiv.org/abs/2506.11097
8. Linehan, L., & Guan, X. (2025, May 26). An analysis of AI Overview brand visibility factors (75K brands studied). Ahrefs. https://ahrefs.com/blog/ai-overview-brand-correlation/
9. Davidson, O. (2026, March 25). The long tail: Where visibility in AI search is won. AirOps. https://www.airops.com/report/the-long-tail-where-visibility-in-ai-search-is-won
10. Google Search Central. (n.d.). AI features and your website. Retrieved October 12, 2026, from https://developers.google.com/search/docs/appearance/ai-features
11. Goodwin, D. (2026, January 28). AI local visibility is up to 30x harder than ranking in Google: Report. Search Engine Land. https://searchengineland.com/ai-local-visibility-report-2026-468085