Shaped

Shaped is an AI-native platform designed to help businesses enhance user engagement, increase conversion rates, and boost revenue through intelligent, real-time recommendations and search. It leverages machine learning to dynamically personalize content and product discovery across digital surfaces, learning continuously from user behavior to deliver the most relevant results. The core value proposition lies in its ability to seamlessly integrate advanced AI into existing applications, enabling companies to offer a tailored user experience without requiring deep in-house expertise in data science or infrastructure management.

Key features: The platform offers configurable recommendation engines for various formats like 'users also liked' or 'trending now,' and a powerful AI-powered search that understands natural language and context. It supports multi-surface learning, meaning it can unify user signals from websites, mobile apps, and other channels to build a comprehensive interest profile. Features include diversity boosting to prevent filter bubbles, model transparency for insight into why items are recommended, and cloud-based autoscaling for handling traffic spikes. For example, a media platform can use it to create personalized article feeds, while a marketplace can rank products based on individual user interest and real-time behavioral signals.

What sets Shaped apart is its focus on being a configurable, end-to-end solution that balances sophistication with developer-friendly deployment. Unlike building in-house systems or using rigid SaaS tools, it provides the flexibility of fine-tuning models with contextual and behavioral signals while handling the underlying ML ops, data integration, and infrastructure scalability. It is built around embeddings and modern recommendation algorithms, offering fast deployment via API. The platform integrates with common data sources and analytics tools, allowing teams to experiment with different models and ranking strategies to continuously optimize performance.

Ideal for product teams and developers at B2C, D2C, media, retail, and social media companies looking to implement or improve recommendation and search systems. Specific use cases include building personalized feeds for content platforms, enhancing product discovery in e-commerce marketplaces, increasing ad relevance, and improving user retention in apps. It is particularly valuable for businesses that need to scale their personalization efforts quickly without a large dedicated ML team.

The platform operates on a freemium model, offering a free tier for getting started with basic features and lower volumes. For production use with higher traffic and advanced capabilities like experiment management and dedicated support, paid enterprise plans are available, with pricing typically scaling based on usage metrics such as monthly active users or request volume.