Shaped

Shaped is a real-time retrieval engine designed for search, personalized feeds, recommendations, and AI agent memory. It enables teams to connect their data, train machine learning models, and query text, user, or session context to retrieve relevant results in milliseconds. The platform combines hybrid search, personalization, and ranking capabilities into a unified, queryable relevance engine that delivers an average 20% increase in conversions.

• ShapedQL Query Language allows developers to compile SQL-like queries into optimized, multi-stage ranking pipelines that retrieve, filter, score, and reorder results with declarative configuration instead of complex nested queries.

• Three-Layer Architecture provides a comprehensive system with a Data Layer (30+ connectors for batch and streaming data), Intelligence Layer (ML models and dynamic embeddings with continuous learning), and Query Layer (real-time retrieval with sub-50ms latency).

• Hybrid Search Capabilities combine semantic search and keyword search across multiple indexes, enabling personalized search results that consider both content relevance and user behavior patterns.

• Native Data Connectors integrate with data warehouses (Snowflake, BigQuery, Redshift, Databricks), analytics platforms (Amplitude, Segment, Rudderstack, Posthog), streaming services (Kinesis, Kafka, Pub/Sub), and catalog storage (Postgres, MongoDB, Shopify).

• Personalized Feed Generation creates "For You" feeds with multi-retriever support, exploration, and diversity optimization that adapt in real-time to user behavior.

• Agent Retrieval powers AI agents with contextual memory and retrieval capabilities for RAG applications, treating user context as a first-class input rather than flattened documents.

• Enterprise Security includes SOC 2 Type II certification, GDPR/CCPA/HIPAA compliance, 99.95% uptime SLA, and support for private networking.

To get started, sign up for a free account with $300 in credits (no credit card required), connect your data sources using native connectors, and begin building ranking models. The platform enables teams to go from data connection to production deployment in approximately 7 days without managing infrastructure.