By Leela Najafi & Zach Quart, Venture Partners
Courtyard Ventures has its eyes on Vertical AI-Powered Search & Discovery Engines Driving Personalization– And Here’s Why.
In this post, we explore the hockey stick impact of technology democratizing business creation, which, while fostering innovation, has also led to a fatiguing paradox of choice. Today, AI-powered solutions attempt to narrow the ever-growing consumer funnel.
We’ll delve into:
The evolution of technology and consumer behavior.
AI’s role in clarity and focus: Exploring how AI is used in search and discovery use-cases as a tool for precise personalization.
Vertically-focused platforms and recys-as-a-service: Drawing from expert opinions such as Mark Abraham and Shwetank Kumar, two thought-leaders in the AI-powered search and discovery space, we delve into key challenges and opportunities
The landscape of thought leadership and funding in AI-powered personalization: including views from leaders in AI, recommender systems, search, and consumer.
A framework for innovators in search and discovery.
Peak Fatigue– New Technology Driving Business Creation & Product Overload
Once, shopping was a deliberate act: leaving your home to browse store aisles, weigh a few options, and make thoughtful purchases. Now, it’s relentlessly vying for your attention with targeted ads, subscription boxes, endless online catalogs. You can’t escape it, and quite frankly—it’s overwhelming.
Much of this transformation can be largely attributed to the explosion of B2B software serving early-stage businesses, including direct-to-consumer (DTC) brands, reducing the cost of launching a business from six figures to as little as $2,000. The result? A booming marketplace, with U.S. DTC sales projected to hit $213 billion this year alone—a staggering 178% increase since 2019.
While this abundance has leveled the playing field for emerging brands, it has also left consumers drowning in choices. The next step? A recalibration. AI-powered solutions are stepping in to reframe the consumer experience, offering organized access paired with deeply personalized experiences.
At Courtyard Ventures, we envision a future of consumerism that balances the abundance of options with simplicity, using AI to deliver precision-tailored solutions mapping individual needs.
The Rise of Infinite Choice & Consumer Preferences in the Modern Era
Take your morning coffee run. What used to be a simple decision—regular or decaf—is now a maze of oat, almond, soy, cashew, and pistachio milk. In health and beauty, DNA-informed supplements and bespoke skincare have replaced one-size-fits-all solutions. The era of uniform consumption—when everyone drank the same soda or wore the same jeans—is gone. Customization is the new standard.
But with this abundance comes a trap: choice paralysis. Instead of feeling empowered, consumers are overwhelmed. Recent research by Forerunner Ventures found that consumer stress has risen 39% year-over-year due to the pressure of navigating endless options. Similarly, Accenture reports that 74% of consumers in 2023 abandoned purchases because they felt overwhelmed.
This tension defines modern consumer behavior. We crave tailored experiences, but the effort required to sift through endless options often feels insurmountable. Scroll fatigue is real, leaving consumers overwhelmed by their options yet paradoxically unsatisfied with their selections. But finding the right item or brand for each person amid the growing noise requires serious effort, underscoring the need for streamlined discovery.
Personalization is Good Business
Personalization offers a way forward. A recent Harvard Business Review study by Mark Abraham and David Edelman highlights the business value of getting it right. Despite two-thirds of surveyed consumers reporting experiences with recommendations that felt inappropriate, inaccurate, or invasive, more than 80% still say they want and expect personalized interactions.
This isn’t just consumer preference; it’s good business. Companies that prioritize personalization in their strategies are growing 10 percentage points faster than their peers. These personalization leaders not only forge deeper digital relationships but also drive higher spending—30% more than the category average. Over time, the investment compounds: $1 placed in a leader yields $3 after five years compared to 50 cents for laggards.
AI’s Role in Organizing Abundance
Delivering personalization at-scale demands innovation. A critical solution to consumer overwhelm lies in AI-powered vertically-focused search and discovery platforms. Unlike traditional unilateral search engines that prioritize breadth, these platforms can deliver depth—providing curated choices that meet unique consumer needs for specific verticals.
Google’s original premise of providing direct, immediate answers remains relevant, but today’s consumers expect more. They want contextually rich, personalized experiences that are as intuitive as your all-knowing friend’s recommendation.
The role of AI in search & discovery can be summarized as follows:
Personalizing Discovery: Cutting through the noise with curated selections powered by recommender systems, like those behind TikTok and Instagram, which process billions of data points in milliseconds to align seamlessly with individual preferences.
Reducing Friction: Simplifying decision-making with tailored product recommendations and virtual assistants, driven by Large Language Models (LLMs).
Enabling Customization: LLMs excel at analyzing complex, domain-specific data—such as customer preferences, product attributes, and contextual signals– allowing brands to create deeply personalized products, services, and experiences tailored to individual needs with a new level of precision.
Personalization has emerged as the central promise of these technologies, and to understand its power, we must take a closer look at the systems making it possible.
A Deep Dive On The Technology Underlying Personalization
“Very few people realize that one of the largest computing systems the world has ever conceived of is a recommender system”
– Jensen Huang, CEO of Nvidia, in an interview with Mark Zuckerberg at Siggraph 2024
Recommender systems (RecSys) are the backbone of AI-driven personalization, offering the solution to consumer overwhelm by creating deeply contextual, tailored experiences. Though the RecSys landscape is complex, there are unmistakable opportunities in this space for builders and investors in a world where personalization is everywhere.
As Jensen Huang noted in a recent conversation with Mark Zuckerberg, the largest computing system ever conceived by humanity is, in fact, a recommender system. Looking ahead, we should expect state-of-the-art RecSys will become foundational for any marketplace, e-commerce, or content-driven business—much like how cloud computing has evolved into an essential baseline for modern operations.
Understanding the winners in this space begins with understanding how these sophisticated systems function. By tracking user behavior over time, recommender systems create a dynamic, foundational understanding of individual preferences and needs. They combine this with a semantic understanding of a catalog’s content—be it products, services, or media—to deliver the most relevant options in milliseconds. This feedback loop strengthens with every interaction, creating a powerful flywheel effect, continuously refining its recommendations based on users behavior.
TikTok’s "For You" feed is a great example. Fueled by a cutting-edge recommendation engine, it predicts and serves hyper-relevant videos, keeping users engaged for an average of over 50 minutes per day, according to a report from the market research firm Insider Intelligence. Similarly, Instagram has invested two decades and billions of dollars into perfecting its recommender system, now one of the largest AI systems globally, processing millions of ranking and recommendation requests every second.
The sophistication of social media platforms’ personalization engines—now extending into e-commerce—raises the bar for e-commerce, marketplace, and content businesses alike. For builders navigating this landscape, there are at least two major issues to navigate:
The complexity and cost of scalable, production-grade RecSys: As Shwetank Kumar, a seasoned Chief Data Officer and investor, points out, these systems demand significant resources and deep technical expertise. However, even small, iterative steps—like moving from random suggestions to clustering and association models—can unlock intuitive user experiences and deliver meaningful business impact.
The subjective nature of search relevance: Doug Turnbull, a leading voice in AI-driven search, aptly describes relevant search as a hidden, subjective challenge that can be extremely difficult to perfect across domains. He warns that many search solutions fail because they underestimate the complexity of matching consumer expectations of relevance, which often requires costly customization and deep domain expertise. Turnbull calls this the “the hidden danger” of search, as the domain-specificity of relevance is often invisible and can mislead teams into believing the problem is simpler than it truly is.
Despite these challenges, vertically-focused AI-powered search and discovery platforms, such as Daydream or Roon, are charging ahead. The domain specificity of relevance makes it difficult for horizontally-focused platforms to provide depth and customization across diverse categories. Vertically-focused platforms, however, can build specialized recommender systems tailored to their niche, delivering the precise, contextual experiences that consumers value. The complexity and rapid evolution of RecSys, paired with the power of the flywheel effect, highlights a crucial opportunity for vertically-focused platforms: becoming a RecSys market leader within your vertical creates a durable competitive moat..
For investors and builders exploring how personalization can combat consumer overwhelm while delivering exceptional user experiences, the opportunity can lie in the interconnected ecosystem of technologies and companies driving this shift. In addition to vertically-focused AI powered search & discovery platforms, at Courtyard we’re also paying close attention to the players providing the tools for building advanced RecSys. This includes the emergence of a new-category, called recommendation-as-a-service, that takes care of the complex ML engineering and AI research involved with creating state-of-the-art RecSys and delivers its product via an API.
What’s Happening in Academia
Emerging research in RecSys is pointing toward two transformative trends: the integration of large language models (LLMs) and the adoption of graph neural networks (GNNs). These advancements promise to redefine what’s possible in delivering deeply personalized, efficient, and contextually relevant recommendations.
The Role of LLMs in RecSys
Large language models (LLMs) are unlocking new potential across the recommendation pipeline, driving significant improvements in multiple areas:
Enhanced Feature Engineering: LLMs generate richer textual features from input data like tags or descriptions, elevating the quality of recommendations. Their ability to handle unstructured or diverse datasets allows for aligning and modeling user interests across varied domains.
Improved Encoding for Cross-Domain Recommendations: By creating better semantic embeddings, LLMs enable more accurate representations of user preferences and item characteristics, facilitating seamless cross-domain recommendations—an area where traditional systems often struggle.
Direct Scoring and Ranking: LLMs streamline workflows by directly ranking items based on relevance. Techniques like prompting or integrating LLMs into ranking layers reduce reliance on pre-defined scoring models, simplifying the recommendation process.
Pipeline Control and Adaptability: LLMs bring flexibility to complex recommendation pipelines, managing tasks such as simulating user interactions and orchestrating ranking stages. This adaptability allows them to adjust in real time to user preferences.
Discovery and Diversity: LLMs excel at surfacing novel items, identifying nuanced patterns in data that traditional models often miss, and creating more diverse recommendations.
However, these benefits come with challenges: high computational costs, longer inference times, and limitations in processing large textual inputs. Addressing these hurdles is critical to making LLMs practical for real-world RecSys applications.
The Promise of GNNs in RecSys
Graph neural networks (GNNs), a type of machine learning model that analyzes relationships in network-like data (e.g., users and items) to make predictions, are emerging as another frontier in RecSys. GNNs leverage the graph structure of user-item interactions to capture complex relationships and dependencies, resulting in:
Better Collaborative Filtering: By propagating information through user-item graphs, GNNs enhance collaborative filtering, enabling more accurate recommendations.
Cold-Start Problem Solving: GNNs infer robust representations for new users or items, alleviating cold-start challenges that plague traditional systems.
Incorporation of Rich Side Information: GNNs seamlessly integrate side information, such as user demographics or item attributes, resulting in more personalized recommendations.
Recent academic research highlights GNN-based models outperforming traditional approaches, setting new benchmarks on public datasets. For example, a team of UC Berkeley students recently developed GNNIE, an award-winning GNN-based "recommender-as-a-service" API. GNNIE delivers more diverse, personalized, and interpretable recommendations, signaling how these systems can scale effectively for real-world use.
A Glimpse into the Future of Consumption: Narrowing the Funnel
According to Crunchbase, five leading AI startups collectively raised over $150 million to address the growing demand for simplicity and personalization. Among them, Daydream, co-founded by Julie Bornstein, secured $50 million in seed funding from investors like Forerunner Ventures and Index Ventures. The platform uses generative AI, machine learning, and computer vision to deliver curated fashion recommendations from over 2,000 brands. Daydream demonstrates how AI can streamline the shopping experience by making choices more accessible and tailored to individual preferences.
Roon, which secured a $15M Series A round, is addressing the challenges of finding relevant and reliable information in the health vertical. Roon is aiming to replace googling and legacy healthcare content sites like WebMD and Healthline, with video-based Q&As on thousands of health issues created by doctors in top medical institutions.
Shaped, which recently secured an $8M Series A round, is notable for providing customers with both advanced recys-as-a-service and behavior-driven semantic search. In doing so, Shaped is democratizing access to the technologies that Google, TikTok, and Instagram have historically leveraged to create the deeply personal experiences responsible for their domination.
Meanwhile, Lily AI, with $71.9 million in funding, bridges the gap between brand-speak and customer needs, ensuring shoppers find products that truly align with their preferences. Tools like Markable AI and Yaysay underscore this trend, empowering influencers and consumers alike to navigate the overwhelming market. Together, these companies highlight how AI is transforming retail by turning abundance into clarity.
The next wave of consumer innovation isn’t about offering more—it’s about offering a tailored and intuitive experience. AI-powered personalization will shape this future by helping consumers access the right options without the overwhelm.
Challenges to Model Development, Growth, and Adoption
We find it important to recognize that while the potential of vertical AI-powered search and discovery platforms is immense, their promise also requires navigating several risks that can impact growth, adoption, and the effectiveness of the model itself:
Customer Acquisition Risk: Vertical platforms often face higher customer acquisition costs compared to horizontal platforms – which benefit from broader network effects and superior market presence. It can be both capital-intensive and time-sensitive establishing strong user loyalty in a niche market.
Model Effectiveness and Relevance: Search and discovery models face a unique challenge of balancing precision and inclusivity. Failing to deliver and maintain accurate, contextually relevant recommendations could erode user trust and retention.
Model Learning Speed: Users expect immediate value. If models cannot quickly personalize recommendations— the platform may risk losing momentum before the flywheel effect of data and interaction can take hold.
Model Adaptation: Consumer interests shift dynamically, often unpredictably. Platforms must ensure their models are agile enough to adapt to these changes in real time, enabling them to offer dynamic discovery experiences without delay.
Consumer Sentiment on AI and Privacy: There are reputational risks around consumer scrutiny of data use and privacy in AI. Platforms can proactively address transparency and ethical concerns, balancing both personalization and consumer trust.
Ecosystem Dependence: Many vertical platforms depend on external APIs, datasets, or cloud infrastructure for critical functionality. Reliance on third-party services may pose certain external vulnerabilities such as cost volatility, service outages, or strategic misalignments.
Economic Downturn Sensitivity: In tougher economic climates, consumers may have a lower willingness to pay for personalization, impacting the perceived value proposition of these specialized vertical solutions.
Addressing these risks requires thoughtful design, transparent consumer engagement, and robust strategic planning to ensure the platform’s long-term success. For investors and innovators, understanding these challenges is essential to crafting resilient business models that anticipate and overcome potential roadblocks.
Our Framework for Builders
Solve for Choice Paralysis: Identify high-friction decision-making moments within your vertical. Whether it’s choosing between products, services, or content, focus on simplifying the user journey. Start with deep consumer research to pinpoint where people feel overwhelmed and ensure your solution directly addresses this pain point.
Think Beyond the Tech – Build for the Ecosystem: Don’t just build a great product—build a network around it. Whether through partnerships, integrations, or community-building, think about how your platform can become a seamless part of your users’ broader ecosystem. For instance, embedding your tool into existing workflows or collaborating with adjacent brands can create stickier, more valuable experiences.
Iterate Intelligently with Data: Your AI will only be as good as the data it learns from. Establish clear feedback loops to ensure your models evolve as your users’ needs change. Invest early in tools for monitoring, analyzing, and optimizing recommendations based on real-world use. This continuous iteration will help sustain relevance over time.
Design for Dynamic Discovery: Build systems that can adapt in real time to shifts in user behavior, preferences, and trends. Consider how your platform might respond to sudden spikes in interest for a product, event, or category. Dynamic discovery isn’t just a feature—it’s a competitive advantage in fast-moving verticals.
Prioritize Ethical AI Practices: Consumers are increasingly concerned about privacy and data ethics. Make transparency a core part of your product design. Show users how their data is being used to enhance their experience, and give them control over personalization settings. Platforms that build trust win long-term loyalty.
Leverage Behavioral Insights for Better UX: Personalization isn’t just about showing the right product; it’s about anticipating how people want to engage. Use behavioral analytics to refine not just what you recommend, but when and how you present it. Small UX tweaks—like surfacing recommendations during specific moments in the user journey—can drive big gains in satisfaction and engagement.
Don’t Overlook Monetization Opportunities: Beyond subscriptions or direct purchases, think creatively about monetization. Could your platform offer premium personalization tiers, API licensing, or revenue-sharing partnerships? Structuring monetization strategies that align with user needs and business growth can unlock long-term sustainability.
Future-proof Your Platform: The world of AI evolves rapidly. Build your stack to be modular and adaptable, so you can integrate emerging technologies like LLMs, GNNs, or multimodal systems as they mature. Staying ahead of tech trends ensures your platform remains competitive and innovative.
Focus on the Flywheel Effect: Establish a strategy to jumpstart the feedback loop between user engagement and model improvement. Consider incentives—such as loyalty programs, gamification, or community-driven contributions—to encourage users to interact with and improve your system.
Deliver ROI for Both Sides of the Marketplace: If your platform serves both consumers and providers (e.g., sellers or creators), be sure to add tangible value to both sides. For example, tools that help providers optimize their listings or learn from the platform's insights can create a valuable cycle of engagement and satisfaction.
At Courtyard Ventures, we believe consumerism's future will be shaped by organized access and personalized curation—qualities that stand out in an era of overwhelm. We applaud innovators tackling this pervasive challenge for consumers to seamlessly discover exactly what they need, when and where they need it.
And be sure to reach out to us if you are innovating in this space!
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