From the Humanswith.ai archive

HumansWith.AI AEO/GEO

Case data Promotion features From hypothesis to LP creation ROI and key indicators Website: https://humanswith.ai/ Focus: Marketing and AI-driven promotion (AEO / GEO) Location: Moscow Coverage: Russia / international projects Budget: From $5,000 per month Project Start: January 2026 Outcome: First traffic and leads fr

Preserved from an earlier website era Case study March 12, 2026

Case data
Promotion features
From hypothesis to LP creation
ROI and key indicators

Website:

https://humanswith.ai/

Focus:

Marketing and AI-driven promotion (AEO / GEO)

Location:

Moscow

Coverage:

Russia / international projects

Budget:

From $5,000 per month

Project Start:

January 2026

Outcome:

First traffic and leads from AI search

Brand Presence in AI Responses and Measurable Results

Increase in branded traffic as an indicator of growing awareness and demand.

Clicks from AI networks and AI search engines (ChatGPT, Perplexity, Gemini, Alice/YandexGPT, etc.).

“Form Submission” conversions (overall and by source).

Several-fold growth in goal completions.

AI Networks Show a Limited Number of Recommendations — It’s Crucial to Be on the Source List

Limited Recommendation List

When a user asks ChatGPT, Gemini, or Alice a question, they receive a ready-made answer with recommendations and links.

Attention Focused on a Few Sources

Responses typically include only a small set of options — that’s where user attention and demand are concentrated.

Need for Presence Beyond Classic Search

A brand’s goal is to appear not only in traditional search results but also in AI network recommendations.

Selecting Relevant Platforms

For a quick start, we focused on platforms most frequently cited in AI sources, including VC.

Tracking AI Usage of Content

Monitored which specific articles appeared in AI responses and how often.

Analyzing Content Effectiveness

Compared topics and formats to identify what works best.

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Stages of GEO Promotion and Publications

Stage 1. Collection and Analysis of AI Prompts
Collected 1,500 user prompts that people ask AI networks when choosing solutions. For the pilot, a sample of ~200 prompts was selected. The prompts were grouped by user decision stage (using the Ben Hunt model), from “what is this” and “why” to “compare options” and “whom to choose.” This helped understand the types of questions users ask AI at different stages of decision-making.
Stage 2. Analysis of AI Sources
Analyzed which sources AI networks use in our segment. Compiled a short-list of platforms most frequently cited in AI responses: VC.ru, Wikipedia (RU/EN), Habr, YouTube, Dzen, Sostav, Adpass, Companies.RBC, LinkedIn, Reddit. Publications were concentrated on these platforms to work with sources that AI networks trust.
Stage 3. Content Plan Development
Content was built based on user prompts and AI source analysis. Formats that AI networks easily pick up were used: explanations, comparisons, guides, risk analyses, and “how to choose” materials. Special focus was placed on rankings and curated lists, as AI tends to use structured lists of solutions. Topics were aligned with user decision stages.
Stage 4. Publishing on External Platforms
Over 2 months (January–February 2026), 36 publications were released: VC — 20, Dzen — 9, Medium — 7. Publication topics included “business from Russia abroad,” “business in Russia,” and commercial queries like “order / buy AEO/GEO.” Some content was published in the form of rankings and comparisons.
Stage 5. Testing Additional Sources
Simultaneously, international platforms and other content formats were tested. Experiments included Reddit, YouTube/Vimeo, and UGC platforms. This expands brand presence in sources that AI networks may reference. Testing is ongoing.
Stage 6. Monitoring AI Mentions and Results
After publication, materials were regularly tracked in AI responses. Monitoring tools included ai-semantica, gptfox, and website analytics. Mentions, AI-driven traffic, and leads were recorded. These insights were used to adjust content topics and formats.

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Impact of Implementing

Inclusion in AI Recommendations

Within 1–3 days after publication, some materials were already recorded as cited in AI responses.

AI Citations and Traffic

Example: two VC publications received 60 and 32 mentions in AI responses. Traffic from AI networks (ChatGPT, Perplexity, Gemini, etc.) began appearing.

New Lead Generation Channel

First inbound leads came in, with clients explicitly noting AI search as the source (“found via ChatGPT/Alice/Perplexity”).