There’s a sandbox at https://play.shaped.ai/dashboard/home that you can use to explore demo models and evaluate results interactively. And we have a demo video at https://www.youtube.com/watch?v=toCsUYQnJ_g.
The explosion of online content, driven by both individuals and generative tools, is making it harder than ever for users to sift through the noise and find what's relevant to them. Platforms like Netflix, TikTok, and Meta have set a high bar, proving that personalized experiences are key to cutting through the clutter and engaging users effectively.
Despite advancements in AI and semantic infrastructure like vector stores, building a truly relevant recommendation or search system is still extremely difficult. It's not just about deploying the latest LLM—the difficulties lie in creating the infrastructure to orchestrate the components seamlessly. Consider the challenge of continuously fine-tuning models with fresh data while simultaneously serving real-time personalized recommendations to millions of users. It requires a delicate balancing act of speed, scale, and sophistication.
Our goal is to empower any technical team to build state-of-the-art recommendation and search systems, regardless of their data infrastructure. Here's how we eliminate the friction:
Solving Data Challenges: We integrate directly with your data sources—Segment, Amplitude, Rudderstack, and more. We handle the complexities of real-time streaming, ETLs, and data quality robustness, so you can get started in minutes.
Leveraging Cutting-Edge Models – we utilize state-of-the-art large-scale language, image, and tabular encoding models. This not only extracts maximum value from your data but also simplifies the process, even with unstructured data.
Real-time Optimization: Unlike vision or NLP tasks, recommendation system performance hinges on real-time capabilities—training, feature engineering, and serving. We've architected our platform with this at its core.
We're already helping many companies build relevant recommendations and search for their users. Outdoorsy, for example, uses us to power its RV rental marketplace. E-commerce businesses like DribbleUp and startups like Overlap have seen up to a 40% increase in both conversions and engagement when integrating Shaped.
A bit about us: Tullie was previously an AI Researcher at FAIR working on multimodal ranking at Meta. He released PyTorchVideo, a widely-used video understanding library, which contains the video understanding models that power systems like IG Reels. Dan led product research at Afterpay and Uber, driven by how behavioral psychology influences user experience.
We've been heads down building Shaped for quite a while, so this launch feels like a big milestone. We'd love to hear your feedback – technical deep dives, feature requests, you name it. Let us know what you think!