# www.shaped.ai

> AI-optimized mirror of www.shaped.ai containing 50 pages totalling 41,993 words of clean markdown content, structured data, and semantic HTML. Original source: https://www.shaped.ai/. Last updated: 2026-04-30T22:28:57.099Z.

Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG).

## Homepage

- [The only vector database with a feedback loop](/site-root.html): Shaped gives your AI agent the right 10 results instead of 200 noisy ones. $0.03 per answer vs $1.50 DIY. The same engine powers personalised search and recommendations. (851 words)

## Articles & Blog Posts

- [blog/a-technical-intro-to-embeddings/index.html](/blog/a-technical-intro-to-embeddings/index.html) (1 words)
- [Activate Your Segment Data for Real-Time AI Personalization with Shaped](/blog/activate-your-segment-data-for-real-time-ai-personalization-with-shaped.html): In a world where personalization drives engagement, capturing cross-channel behavior is only half the battle — the real value lies in acting on it instantly. Segment unifies customer data from every touchpoint, but to unlock its full potential, you need intelligence layered on top. That’s where Shaped comes in — turning your Segment event stream into real-time recommendations, personalized search, and predictive insights with minimal setup. In this post, we’ll walk through how to integrate Segment with Shaped using AWS Kinesis and start delivering smarter, more adaptive user experiences right away. (1,662 words)
- [privacy-policy/index.html](/privacy-policy/index.html) (1 words)
- [Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation](/blog/action-is-all-you-need-dual-flow-generative-ranking-network-for-recommendation.html): TL;DR: Meta's generative recommender (MetaGR) is powerful but slow. Researchers from Meituan and top universities just dropped DFGR, a dual-stream architecture that's 2x faster at training and 4x faster at inference, while also beating MetaGR and heavily-engineered industrial models on ranking accuracy. (1,543 words)
- [Activate Your Rudderstack Event Streams for Real-Time AI Personalization with Shaped](/blog/activate-your-rudderstack-event-streams-for-real-time-ai-personalization-with-shaped.html): This post explores how teams can connect Rudderstack, a developer-focused Customer Data Platform, to Shaped using AWS Kinesis to power real-time personalized recommendations and search. Rudderstack unifies event data across web, mobile, and backend sources, while Shaped ingests these streams to train and update machine learning models on the fly. The article breaks down the benefits of this integration, like dynamic “For You” feeds and context-aware search, and provides a step-by-step guide to setting up the connection, enabling teams to activate their event data for intelligent user experiences without managing complex ML infrastructure. (1,712 words)
- [Activating Your Apache Iceberg Data Lake for AI Personalization with Shaped](/blog/activating-your-apache-iceberg-data-lake-for-ai-personalization-with-shaped.html): Apache Iceberg is revolutionizing data lake management with features like ACID transactions, schema evolution, and time travel, making it ideal for reliable analytics. But turning that structured data into real-time, AI-powered personalization still poses challenges. This article shows how Shaped’s native Iceberg connector bridges that gap: it connects directly to Iceberg tables (via Glue or Hive catalogs), ingests consistent snapshots, and trains advanced ML models for personalized recommendations and search, all without building complex pipelines. Learn how to activate your Iceberg data for intelligent, real-time experiences with minimal setup. (1,874 words)
- [blog/ab-testing-retrieval-how-to-prove-your-agent-is-getting-better.html](/blog/ab-testing-retrieval-how-to-prove-your-agent-is-getting-better.html) (1 words)
- [Activating Your Redshift Data Warehouse for AI Personalization with Shaped](/blog/activating-your-redshift-data-warehouse-for-ai-personalization-with-shaped.html): Amazon Redshift is a powerful warehouse for analytics, but using that data for real-time personalization often requires complex pipelines. This article shows how Shaped's Redshift connector bridges that gap by securely syncing structured data, like user histories and product metadata, directly from Redshift. It enables teams to train machine learning models and serve AI-powered recommendations and search results via API, all without duplicating infrastructure or overloading the warehouse. Learn how to activate your Redshift data for intelligent user experiences with minimal setup. (1,765 words)
- [blog/10-best-practices-in-data-ingestion/index.html](/blog/10-best-practices-in-data-ingestion/index.html) (1 words)
- [Activate Your S3 Data Lake for AI Personalization with Shaped](/blog/activate-your-s3-data-lake-for-ai-personalization-with-shaped.html): Amazon S3 is great for storing batch data, but turning that static data into dynamic, personalized experiences usually requires heavy ML infrastructure. With Shaped’s direct S3 connector, you can skip the complexity. Simply point Shaped to your S3 bucket (CSV, Parquet, JSONL, etc.), and it will automatically ingest data, train relevance models, and serve real-time personalized recommendations and search via API. No pipelines, no custom loaders, just plug in your data lake and activate it for intelligent discovery. (1,949 words)
- [Understanding Graph Convolutional Neural Networks for Web-Scale Recommender Systems](/blog/a-new-graph-convolutional-neural-network-for-web-scale-recommender-systems.html): In this article, we'll take a deep dive into PinSage, a state-of-the-art GCN framework developed at Pinterest for learning high-quality embeddings of nodes in massive, billion-scale graphs. Through a novel combination of techniques spanning sampling, dynamic graph construction and distributed computing, PinSage achieves order-of-magnitude speedups over existing GCN approaches while delivering substantial gains in recommendation performance. Understanding the innovations powering PinSage provides a window into the frontier of deploying deep learning on web-scale systems. (1,499 words)
- [commercial-terms/index.html](/commercial-terms/index.html) (1 words)
- [terms-of-service/index.html](/terms-of-service/index.html) (1 words)
- [Activating Your MongoDB Data for AI Personalization with Shaped](/blog/activating-your-mongodb-data-for-ai-personalization-with-shaped.html): MongoDB is a flexible, developer-friendly database widely used for storing rich, evolving user and product data. But turning that operational data into real-time personalization has traditionally required complex ETL and custom ML pipelines. With Shaped’s native MongoDB connector, you can skip the heavy lifting. Shaped ingests schemaless MongoDB documents directly, transforms them for training, and serves personalized recommendations and search via simple APIs, bridging the gap between document stores and intelligent, AI-powered user experiences. (1,906 words)
- [Activating ClickHouse Data for AI-Powered Personalization with Shaped](/blog/activating-clickhouse-data-for-ai-powered-personalization-with-shaped.html): ClickHouse is unmatched when it comes to high-performance analytics at scale, trusted for powering fast, flexible querying across billions of events. But what if the same data fueling dashboards could also drive real-time personalization? That’s where Shaped comes in. By connecting ClickHouse to Shaped, teams can transform rich historical interaction data into intelligent recommendations, personalized search, and predictive insights, without straining their analytics stack. This post explores how ClickHouse and Shaped together unlock the full potential of your event data. (1,770 words)
- [A ranking model for every use-case!](/blog/a-ranking-model-for-every-use-case/index.html): Introducing Shaped's "Model API". A configuration API to Shaped's multi-stage recommendation system. (1,347 words)
- [Customized email content that drives clicks](/personalized-email/index.html) (761 words)
- [A/B Testing Your Rankings: Metrics That Matter in the Real World](/blog/a-b-testing-your-rankings-metrics-that-matter-in-the-real-world.html): You’ve trained a model, optimized offline metrics, and picked a winner, but how do you know it’ll perform with real users? In this post, we explore why A/B testing is essential for validating personalization and ranking models in production. We cover key online metrics like CTR, CVR, and North Star Metrics, how to design statistically rigorous experiments, and how Shaped makes it easy to deploy, bucket, and measure real-world impact. (1,574 words)
- [Introducing Shaped for Agents — retrieval that gets it right the first time](/for-you-feeds/index.html) (805 words)
- [blog/context-window-optimization-why-ranking-not-stuffing-is-the-scaling-law-for-agents.html](/blog/context-window-optimization-why-ranking-not-stuffing-is-the-scaling-law-for-agents.html) (1 words)
- [Shaped powers relevance for the world’s leading platforms](/case-study/index.html): Shaped gives your AI agent the right 10 results instead of 200 noisy ones. $0.03 per answer vs $1.50 DIY. The same engine powers personalised search and recommendations. (139 words)
- [blog/ai-powered-recommendation-engines/index.html](/blog/ai-powered-recommendation-engines/index.html) (1 words)
- [blog/content-based-filtering-explained-recommending-based-on-what-you-like.html](/blog/content-based-filtering-explained-recommending-based-on-what-you-like.html) (1 words)
- [blog/the-7-best-rag-apis-for-personalization-in-2025/index.html](/blog/the-7-best-rag-apis-for-personalization-in-2025/index.html) (1 words)
- [5 Best APIs for Adding Personalized Recommendations to Your App in 2025](/blog/5-best-apis-for-adding-personalized-recommendations-to-your-app-in-2025.html): In 2025, personalization is no longer a nice-to-have. Users expect smart, adaptive recommendations in every app — from e-commerce and streaming to social platforms, marketplaces, and learning apps. Whether it’s suggesting the next video, surfacing relevant products, or ranking news feeds, personalization drives engagement, retention, and revenue. The problem is that building recommendation systems from scratch is notoriously difficult. Companies like TikTok, Netflix, and Amazon employ hundreds of engineers to maintain pipelines for data collection, embeddings, ranking, and optimization. (1,012 words)
- [Cart Upsell that feels helpful, not pushy](/cart-upsell/index.html) (768 words)
- [Hybrid Search that finds exactly what users mean](/hybrid-search/index.html) (782 words)
- [Personalized Product Recommendations that convert](/product-recommendations/index.html) (766 words)
- [E-commerce](/solutions/e-commerce/index.html) (286 words)
- [A control panel for your business objectives](/value-modeling/index.html) (285 words)
- [Media Platforms](/solutions/media-platforms/index.html) (295 words)
- [Introducing Shaped for Agents — retrieval that gets it right the first time](/generative-enrichment/index.html) (334 words)
- [How Does Temu Work? Understanding Its Personalization Strategy](/blog/how-does-temu-work/index.html): This article examines how Temu became one of the fastest-growing e-commerce platforms by using AI to fuel engagement across the user journey. It explores how real-time deep learning models, gamification, and multi-objective optimization drive personalization, session depth, and customer loyalty. (1,857 words)
- [$1.9M Funding Round](/blog/1-9m-funding-round/index.html): Shaped is proud to announce our $1.9 million funding round! (609 words)
- [Context-aware retrieval for AI Agents](/agent-retrieval/index.html) (400 words)
- [A unified analytics toolkit built for search and recommendations](/analytics/index.html) (178 words)
- [Introducing Shaped for Agents — retrieval that gets it right the first time](/semanticsearch/index.html) (281 words)
- [Three-layer architecture](/pricing/index.html) (402 words)
- [Introducing Shaped for Agents — retrieval that gets it right the first time](/solutions/social-media/index.html) (293 words)
- [Marketplaces](/solutions/marketplace/index.html) (297 words)
- [Introducing Shaped for Agents — retrieval that gets it right the first time](/contact/index.html) (241 words)
- [Introducing Shaped for Agents — retrieval that gets it right the first time](/cloud/index.html) (321 words)
- [Why Shaped is the #1 Product Recommendation Engine](/blog/why-shaped-is-the-best-product-recommendation-engine.html): Most recommendation tools are either bolted-on search engines or basic e-commerce widgets. Shaped is different. It’s a product discovery engine built from the ground up to power personalized, ML-driven rankings across every surface of your user experience, not just carousels. With deep understanding of product data (images, text, behavior), flexible APIs, and full transparency, Shaped gives teams the power to control, personalize, and scale their entire product discovery journey. (1,329 words)
- [The 10 Best AWS Personalize Alternatives in 2025](/blog/the-10-best-aws-personalize-alternatives-in-2025/index.html): AWS Personalize has been around for years as Amazon’s machine learning solution for powering recommendations. It offers teams a way to tap into some of the personalization techniques used at Amazon.com. However, developers often find it expensive, complex to operate, and slow to integrate, especially compared to the newer generation of personalization APIs. In 2025, teams building modern apps want faster integration, more flexibility, and personalization that works across multiple use cases like feeds, search, and recommendations — not just e-commerce. If you are exploring AWS Personalize alternatives, there are now many stronger options that provide speed, developer-friendliness, and better results out of the box. Here are the 10 best AWS Personalize alternatives in 2025, starting with Shaped, the most developer-friendly personalization API available today. (1,219 words)
- [Blog](/blog/index.html): Insights on AI-powered recommendations, search, and personalization from the Shaped engineering team. (4,663 words)
- [How to Implement Effective Caching Strategies for Recommender Systems](/blog/effective-caching-strategies-for-recommender-systems.html): In the realm of recommendation systems, performance and speed are crucial. This post will guide you through advanced caching strategies designed to optimize data retrieval and minimize latency in your recommendation system. Implementing these techniques can greatly improve response times, enhance user experience, and allow your platform to handle large-scale data more efficiently. (1,555 words)
- [Shaped Raises Series A, Launches Self-Serve Platform to Power the Future of AI-Driven Recommendations & Search](/blog/shaped-raises-series-a/index.html): We are thrilled to announce that Shaped has secured $8 million USD in Series A funding to make AI-powered personalization radically easier. The round was led by Madrona Ventures with participation from Y-Combinator and executives and founders from Clickhouse, Docusign, Okta, Rippling, and StitchFix. (1,056 words)
- [Introducing Shaped for Agents — retrieval that gets it right the first time](/recommendations/index.html) (277 words)
- [sitemap-xml.html](/sitemap-xml.html) (1,320 words)

## Resources

- [Full Page Index](/index.html): Browse all cached pages with rich metadata
- [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines
- [XML Sitemap](/content/sitemap.xml): Machine-readable sitemap for crawler discovery
- [Robots.txt](/content/robots.txt): Crawler directives