Shaped
The fastest path to relevant recommendations and search
Shaped launched on July 22, 2024 with 362 votes, #126 of 5,184 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Shaped provides configurable recommendation and search tools that adapt in real-time to user behavior. Built for developers, the platform helps businesses increase engagement, conversion, and revenue by delivering relevant content and product suggestions. It combines artificial intelligence with flexible configuration options, allowing teams to customize how recommendations appear and function across their applications without extensive custom development.
written from the facts on this page · September 2026
From the sources
Increase engagement, conversion, and revenue with configurable recommendations and search that adapt in real-time
The only vector database with a feedback loop, powering personalised search and recommendations. Shaped has been acquired by Whatnot.
Connect your data. Shaped handles the embeddings, models & data freshness. Retrieve relevant results in milliseconds. Retrieve by text, user ID, or item ID — whatever context you have. Unlike a vector store, Shaped knows who's asking. Results are personalised per user and get better with every interaction. Blend semantic and keyword search, reranked by what this user actually clicks. Also available via Python SDK, TypeScript SDK, or MCP. See docs Shaped is an end-to-end relevance engine designed for real-time personalization and agent memory. Personalized content feeds that adapt in real-time to user behavior. Ingest raw event logs directly from your warehouse or real-time streams. No ETL…from shaped.ai
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Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic…
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When OpenAI released Learning Transferable Visual Models From Natural Language Supervision in 2021, it sparked a multimodal semantic search revolution. While the concept wasn’t new, the paper catalyzed a shift in the industry, inspiring a wave of semantic search tools. These advances delivered a leap forward in search quality compared to traditional lexical search, with adopters reporting significant improvements in conversion rates and revenue. But semantic search alone isn’t the endgame. Productionizing search remains a challenge, and hybrid search—combining keyword precision with semantic…
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