From Search to Recommendation: How AI Is Changing the Way Consumers Discover Brands

What this case study is about

Avni Wellness is a menstrual-care brand. AutoAgent worked with the brand on Generative Engine Optimization (GEO): making its products understandable to AI systems that answer consumer questions directly.

Traditional search gives consumers a list of options and leaves most of the research to them. Conversational AI changes that: a consumer can describe their situation and ask what they should choose.

So the opportunity for Avni was not simply ranking for a product keyword such as "menstrual cup". The opportunity was to make Avni's products understandable in relation to the things an AI system has to reason about before it can answer.

AI recommendation requires more than product-name recognition; it requires contextual information about the product, use case, evidence and alternatives.

• Consumer problems
• Use cases
• Product categories
• Evidence
• Comparisons
• Recommendations

The new consumer decision journey

A consumer might once have searched "best sanitary pads for sensitive skin" and then worked through the results themselves.

In that journey the consumer is responsible for doing most of the research.

AI models such as ChatGPT and Gemini change the journey. A consumer can instead ask: "I have sensitive skin and get irritation during my period. What sanitary products should I consider?"

The consumer is no longer simply asking "Where can I find information?" They are asking "Based on my situation, what should I choose?"

Why AI search feels different

Traditional search gives the consumer a set of options. Conversational AI attempts to interpret the question and construct an answer.

That makes the interaction feel more like a conversation than a conventional search session.

Research into conversational AI has found that factors including social presence, conversational relevance, personalization and rapport can influence how users interact with AI systems and how willing they are to disclose information. Studies have also explored how users can perceive conversational agents as social or relational actors rather than simply information-retrieval interfaces.

This matters particularly when the subject is personal — as it is in menstrual care.

AI is becoming part of the research process

This does not mean people automatically trust everything AI says. They don't. But the interaction creates a different research behaviour: instead of opening ten tabs, a consumer can keep asking.

The research process happens inside the conversation. That creates a new opportunity and a new distribution channel for brands whose information is easy for AI systems to use.

What AI needs before it can recommend a product

A recommendation is usually the final output of several smaller understandings. Consider a simple consumer question: "What should I use for heavy periods at night?"

If a brand's website only says something like "our product is best for heavy flow", the AI still has to work out the rest on its own.

Questions the AI is left to answer: Who is this for? What problem does it solve? When should I recommend it? Why this product instead of another one? That is where many product pages fall short.

From product content to problem content

Take the example of a menstrual cup.

Now imagine the consumer asks: "What menstrual product can I use while travelling?"

The product is no longer just associated with the keyword "menstrual cup". It is associated with a specific use case. The same principle applies across a product catalogue.

Why comparison content matters

AI recommendations are rarely made in isolation. Consumers ask which is better, what the difference is, whether they should choose A or B, and whether something is worth it. A product therefore needs information that helps AI understand its position relative to alternatives.

The objective is not to say "Avni is always better". It is to make the decision criteria explicit — for people and for AI systems alike.

How AI recommendations require evidence

A brand shouldn't expect AI to recommend a product based only on marketing claims. AI needs accessible information such as:

These signals give AI systems more accessible context from which to understand and evaluate a product. They do not guarantee a recommendation.

• Product specifications
• Materials
• Usage instructions
• Product reviews
• Certifications and testing where applicable
• Comparisons
• FAQs
• Brand information
• Category expertise
• Consistent information across the website

The information graph behind a recommendation

There is an important part of AI visibility that consumers never see. Before an AI answer can suggest "consider Avni", the system needs enough information to construct that answer.

GEO is not simply about getting mentioned. It is about making the brand easy to understand and easy to justify.

AI-referred traffic increased by 100%

The change was also visible in Avni's analytics.

• Increased engagement time from AI-sourced visitors
• Stronger engagement overall
• Higher conversion performance compared with other organic sources

From ranking for keywords to owning problems

This is the broader lesson from the case. Traditional SEO often starts with: "What keyword should we rank for?"

AI discovery increasingly requires another question: "What problem should AI associate this brand with?"

For a brand like Avni, those problems could include:

The brand becomes discoverable through the problem, not only through the product name.

• Sensitive-skin menstrual care
• Reusable menstrual protection
• Heavy-flow period protection
• Pad-free periods
• Period-product cleaning
• Period-stain removal
• Sustainable menstrual care

Understand → Match → Prove → Recommend

The Avni project led us to a simple framework for AI recommendation readiness.

AI doesn't need another brand description. It needs enough context to make a decision.

What brands can learn from Avni

• Build content around problems, not only products.
• Make use cases explicit.
• Give AI enough evidence to evaluate products.
• Make comparisons and decision criteria clear.
• Maintain consistent brand and product information across the site.
• Structure content so both people and machines can understand it.
• Measure AI-referred traffic and downstream engagement, not only traditional search traffic.

Frequently asked questions