How to outrank, outsell, and outperform in AI search with Pattern’s GEO Scorecard

Five years ago, growing your brand online meant fine-tuning keywords and vying for prime spots in search results. Now, the game has changed: AI platforms are transforming how shoppers discover products, get recommendations, and make decisions.

Today, instead of typing “best running shoes,” consumers simply ask their favorite AI, “What running shoes should I buy?” These models respond quickly, often drawing on vast stores of user-generated content, reviews, and discussion threads—a dynamic “source stack” that looks very different from traditional SEO rankings. What’s more, they can answer highly specific, long-tail questions, from “Which running shoes are best for flat feet in rainy climates?” to “What sneakers do marathoners recommend in 2024?”

But this shift introduces new questions:

• When a chatbot answers a shopper’s question, does it mention your brand—or your competitor?
• How do LLMs even know who you are?
• What’s missing from your product content or reviews that could make your brand “invisible” to these new recommendation engines?

Traditional SEO can’t answer those questions. And without insight into these AI-driven conversations, brands risk becoming invisible where it matters most.

Pattern is a leader in ecommerce acceleration, helping brands navigate the complexities of selling on marketplaces and achieve profitable growth through a combination of proprietary technology and on-demand expertise. Pattern was founded in 2013 and has expanded to over 2,100 team members in 23 global locations, addressing the growing need for specialized ecommerce expertise.

Pattern has over 46 trillion proprietary ecommerce data points, 29 patents and patents pending, and deep marketplace expertise. Pattern partners with hundreds of brands, like Nestlé and Philips, to drive revenue growth. As the top third-party seller on Amazon, Pattern uses this expertise to optimize product listings, manage inventory, and boost brand presence across multiple services simultaneously.

In this post, we share how Pattern uses AWS services to power their recent release.

Why AI recommendations matter now

Just as SEO redefined ecommerce, AI is rewriting the rules for search. Nearly 60% of searches end without a click, a trend driven by zero-click searches and AI summaries. According to a recent survey by Capgemini, 58% of consumers now (as opposed to 25% in 2023) use AI to discover and research products, making AI optimization the next frontier for digital commerce.

“Just as brands once had to master search engine optimization, they now need to understand how generative AI engines like ChatGPT are presenting their products,” said Dave Wright, co-founder and CEO at Pattern. “AI is redefining how consumers discover and shop and brands that don’t adapt risk being left behind. The GEO Scorecard gives them the visibility and tools to win in this new era of product discovery.”

Pattern’s data shows that if leading language models aren’t recommending your brand, you’re missing out on a rapidly growing share of customer attention. With limited transparency into how AI platforms make their picks, brands need new tools to understand and optimize for this crucial new channel.

What is GEO and why does it matter?

Generative Engine Optimization (GEO) is the evolution of search optimization for the AI era. While SEO focuses on ranking in Google’s search results, GEO helps ensure your brand appears with favorable recommendations in AI-powered conversations across leading platforms, including ChatGPT, Gemini, Grok, and others.

Pattern’s GEO Scorecard is built specifically for this AI-powered, conversation-first era. Leveraging Pattern’s proprietary technology and over 46 trillion commerce data points, it provides brands with visibility into how major large language models perceive, rank, and describe their products.

The GEO Scorecard reveals:

• If, where, and how often AI recommends your brand across these leading platforms
• How you benchmark against top competitors in real-time AI recommendations
• Which domains and sources AI is searching, so you can focus your efforts where they matter most
• Step-by-step, actionable recommendations to increase your share of voice in AI-driven discovery

How Pattern built GEO on AWS

Pattern’s GEO Scorecard leverages AWS services to process billions of data points and deliver real-time insights into AI-powered brand visibility across multiple platforms.

Application infrastructure
The GEO Scorecard runs on Amazon Elastic Container Service (Amazon ECS) using AWS Fargate Serverless Compute Engine, providing serverless compute that automatically scales based on demand. An Application Load Balancer distributes traffic and enables zero-downtime deployments, ensuring consistent performance whether analyzing a single brand or processing hundreds of scorecards simultaneously.

AI orchestration with Amazon Bedrock

At the heart of GEO’s intelligence is Amazon Bedrock, specifically leveraging Anthropic’s Claude models running on Bedrock. The AI model powers multiple parts of the complex workflow that creates the scorecard:

• Generates hundreds of targeted queries based on brand-specific topics and Pattern’s proprietary keyword data
• Analyzes competitor sentiment and brand positioning using natural language processing
• Recursively scans brand websites via agentic tool calling and generates actionable content recommendations, including FAQ pages, blog topics, and URL optimizations

By using Amazon Bedrock models in AI agents running on Amazon ECS, Pattern benefits from the scalable infrastructure offered by AWS, eliminating the need to build and maintain finite on-premises GPU hardware for accessing state-of-the-art language models.

As shown in the diagram below, Brands access Pattern’s GEO platform through a front end application, which then connects directly to Amazon Bedrock through an API (using Anthropic’s Claude LLM). Pattern’s AI Capabilities stack does the rest, returning curated results.

Pattern’s deployment of GEO using Amazon Bedrock on AWS

Data layer

Pattern’s GEO Scorecard relies on Amazon S3 and Amazon DynamoDB working together to store and retrieve massive amounts of information. Amazon S3 serves as the data lake for raw query results, AI responses, and historical scorecard data, enabling Pattern to maintain comprehensive records of brand performance over time. Amazon DynamoDB provides fast, flexible database capabilities for real-time data access, storing structured scorecard metrics, brand rankings, and sentiment scores with single-digit millisecond response times.

Scalability and reliability

This cloud-native architecture enables Pattern to create over 500 brand scorecards in the first quarter of launching GEO Scorecard with minimal infrastructure investment, deliver real-time insights to brands, and iterate rapidly on new features. By leveraging AWS’s global infrastructure, Pattern focuses on delivering value to brands rather than managing servers and databases.

Conclusion

Pattern’s GEO Scorecard demonstrates the power of AWS in revolutionizing AI-driven commerce optimization. By using services like Amazon Bedrock, Amazon ECS, and DynamoDB, Pattern delivers actionable insights that help brands understand and improve their presence in AI-powered product recommendations.

Inspired to build your own innovative, high-performance solution? Explore the entire suite of services that AWS has to offer and discover how you can harness the cloud to bring your ideas to life. To learn more about how the GEO Scorecard could help your brand optimize its AI visibility, visit https://www.pattern.com/solutions/geo-scorecard.