Shaped

Categories: Data Analysis, Automation / Agents, Business & Sales | Pricing: Freemium | Official Website ↗

Shaped is a real-time context engine that unifies retrieval, ranking, and learning to power personalized search, recommendations, and AI agent memory.

Shaped is a real-time context engine designed for personalized content delivery and AI agent memory. It unifies retrieval, ranking, and continuous learning within a single query interface, ShapedQL. The platform aims to reduce costs and improve accuracy for RAG applications by providing relevant context instantly, eliminating retry loops and hallucinations common in traditional agent stacks. The architecture consists of a Data Layer with 30+ connectors for batch and streaming data, an Intelligence Layer for ML models and dynamic embeddings, and a Query Layer for real-time retrieval and ranking with sub-50ms latency. Shaped offers a feedback loop mechanism, allowing results to improve with every user interaction, and supports hybrid search, filtering, scoring, and reordering capabilities.

Key Features

Real-time retrieval and ranking (<50ms latency)
ShapedQL (SQL interface for queries)
Continuous learning with feedback loop
Dynamic embeddings and ML models
30+ native data connectors (batch & streaming)
Hybrid search (semantic & keyword)
Personalized content feeds
Contextual memory for AI agents

Pros

Significantly reduces RAG costs compared to traditional stacks
Improves ranking accuracy and hit rates
Provides personalized results based on user context and behavior
Unifies retrieval, ranking, and learning in one system
Fast deployment to production (under 7 days)

Cons

Standard plan has a minimum usage fee of $500/month
Pricing is usage-based, which can be complex to estimate
BYOC (Bring Your Own Cloud) option is not yet available
Requires integration with existing data stacks
Focuses on specific use cases like search, recommendations, and agents

Use Cases

Personalized content feeds
Hybrid search and discovery
AI agent retrieval and memory
Similar item recommendations
Dynamic personalized email content
Context-aware recommendations for conversational AI

Best For

Product teams
Engineering teams
E-commerce platforms
Social media platforms
Media platforms

Integrations: Snowflake, BigQuery, Redshift, Databricks, Amplitude, Segment, Rudderstack, Posthog, Kinesis, Kafka

Platforms: Web

Watch demo on YouTube ↗

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