AI reshapes e-commerce search: Startup Lantern helps brands embrace the era of "generative engine optimization"

Event Overview

When consumers start asking ChatGPT or Google AI Overviews "what are the best noise-canceling headphones?", will your brand appear in the recommendations? For Andrew Lissimore, founder of Headphones.com, the answer is no. This discovery prompted him to found the startup Lantern — initially focused on customer loyalty, but now fully pivoted to helping e-commerce brands deal with the traffic revolution brought by AI search.

In 2025, Lantern raised a $3.1 million seed round led by Salesforce Ventures, and hired a team of former Amazon engineers to reposition its product as a "Generative Engine Optimization (GEO)" tool. At its core, it has trained an internal model that can predict how a brand's products will perform in AI-driven queries and provide optimization recommendations. The basic version of the tool costs $99 per month, with enterprise pricing negotiable.

Market Background

Search traffic is the core pillar for e-commerce brand discovery, customer acquisition, and sales. Traditional SEO (Search Engine Optimization) is built on the rules of traditional search engines like Google, but the rise of AI is fundamentally changing this landscape.

• AI Search Popularization: Google launched AI Mode, and tools like Perplexity and ChatGPT have become new entry points for consumers to get product recommendations. Users no longer filter results through keyword lists but directly ask AI, which generates answers from limited information sources.
• Rise of GEO and AEO: The industry has begun using "Generative Engine Optimization (GEO)" and "Answer Engine Optimization (AEO)" to describe new optimization strategies. Brands need to ensure that product information can be crawled, understood, and prioritized by AI models.
• Intensified Competition: Startups like Jasper AI and Daydream have launched GEO tools, and the market is quickly becoming crowded. Lantern's differentiator is its "pure focus on e-commerce," concentrating on the performance of individual products rather than overall brand visibility.

Platform and Brand Impact

Platform Level

• Google: Through AI Overviews and AI Mode, Google is transforming search results from link lists to direct answers. This means a redistribution of traffic for e-commerce platforms that rely on search traffic (such as Shopify, Amazon).
• Amazon: As the largest product search engine, Amazon is also testing AI shopping assistants (such as Rufus). Brands must simultaneously deal with multiple rules of traditional search and AI recommendations.
• Emerging AI Platforms: General AI tools like ChatGPT and Claude are becoming new "super intermediary pages." Whether a brand's product data is included in the training of these models directly determines exposure opportunities.

Brand Level- Change in Optimization Targets: Brands used to need to optimize page titles, meta descriptions, and keyword density; now they need to optimize structured product data, user reviews, brand authority signals, and the probability of being cited by AI models. - Budget Reallocation: The marginal benefits of traditional SEO investment are declining, and brands are beginning to shift part of their budget to GEO tools and AI content strategies. Lantern's monthly fee of $99 indicates that small and medium-sized enterprises can also afford basic AI optimization services. - Difficulty in Measuring Effectiveness: The "black box" nature of AI search makes it harder to track optimization results, and brands need new analytical metrics. The emergence of tools like Lantern fills this gap.

Sellers and Consumers

• Squeeze on Small and Medium Sellers: Top brands are more easily cited by AI, and opportunities for long-tail product exposure may further decrease. Lantern's focus on "niche e-commerce" strategy may help small and medium sellers gain differentiated advantages.
• Faster Consumer Decision-Making: AI directly provides recommendations, shortening the comparison process, but it may also reinforce the Matthew effect of brands. Consumer trust in AI recommendations becomes a key variable.

Consumer Trend Analysis

• From Search to Conversation: Consumers are more accustomed to asking questions in natural language rather than entering keywords. For example, "recommend a Bluetooth headset suitable for running" replaces "best running earphones". Brands need to ensure product descriptions include contextual information.
• Trust Shift: Early users were cautious about AI recommendations, but as AI accuracy improves, trust gradually rises. Business Insider cites Morgan Stanley's prediction that Agentic commerce will drive e-commerce growth.
• Mobile-First: AI assistants are more prevalent on mobile devices; brands need to optimize mobile content structure and loading speed.

Regional Market Impact

• North America: The AI search transformation first appeared in English-speaking markets. As the headquarters of Google and OpenAI, brands react fastest and competition is most intense. Startups like Lantern primarily serve the US market.
• Europe: Data regulations like GDPR may limit AI models' scraping of brand data, but European consumers' acceptance of AI shopping assistants is increasing.
• Asia: The Chinese market already has AI search products like Baidu's ERNIE Bot and Alibaba's Tongyi Qianwen; brands need to adapt to the local AI ecosystem. Southeast Asian markets rely more on social e-commerce and live streaming, and the impact of AI search is currently secondary.
• Middle East and Latin America: AI infrastructure construction is relatively lagging, but mobile applications are growing rapidly. They may become new battlegrounds for GEO in the next 2-3 years.

Future Trends1. Rise of Agentic Commerce: AI agents will autonomously complete shopping tasks, and brands need to win the "trust of agents". The founder of Lantern believes that e-commerce brands need to prepare for agent-based shopping now. 2. Data Sovereignty Battle: AI model training relies on high-quality e-commerce data, and the data game between brands and platforms will intensify. Brands' "right to be cited" may become a new commercial asset. 3. GEO Tool Ecosystem Differentiation: Vertical tools like Lantern will increase, while large marketing platforms (e.g., Salesforce, HubSpot) may integrate GEO functionality. The market will see a coexistence of "general tools + vertical experts". 4. Content Strategy Restructuring: Brands will increase AI citation rates by outputting authoritative content (e.g., professional reviews, user cases), and the boundary between content marketing and GEO will gradually blur.