The startup develops deep-learning software that enhances user engagement through personalized feeds, notifications, and recommendations. By utilizing artificial intelligence and machine learning, the platform enables businesses to analyze user behavior and optimize content delivery, increasing the likelihood of user interaction and purchases.
Funding
$10M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
MFounders
Product
Problem
Many platforms struggle to deliver relevant content and product recommendations due to limitations in adapting to real-time user behavior and integrating diverse data sources. Existing solutions often require extensive manual data transformation and lack the flexibility to incorporate custom machine learning models, resulting in suboptimal user experiences and missed revenue opportunities.
Solution
Shaped provides an AI-native personalization platform that enables product and engineering teams to rapidly build and experiment with AI-powered search, recommendations, and ranking systems. The platform connects directly to data warehouses and supports SQL-based feature engineering, allowing for real-time adaptation to user behavior. Shaped's unified architecture merges search and recommendations into a single system, facilitating cross-learning and continuous improvement. By offering a configurable relevance engine and a library of state-of-the-art models, Shaped empowers teams to optimize for business-specific KPIs and deliver personalized experiences across various touchpoints.
Target Audience
Shaped is designed for product and engineering teams in marketplaces, social media platforms, media platforms, and e-commerce businesses who need to deliver personalized search and recommendation experiences at scale.
Features
- Real-time adaptability using behavioral signals for dynamic re-ranking
- Unified search and recommendation system for cross-learning and improved relevance
- Direct integration with data warehouses such as BigQuery, PostgreSQL, MySQL, and Snowflake
- Support for SQL-based feature engineering and custom machine learning models
- Library of state-of-the-art models, including transformer models and hybrid embeddings
- A/B testing capabilities for validating model performance in production
- Connectors for ingesting data from MongoDB and other data sources
- Real-time analytics and performance metrics for monitoring and optimization
- Value Modeling to integrate business objectives into recommendations