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What is Shaped?
Shaped is a real-time context engine designed for agentic AI, built by a team focused on reducing the cost and complexity of AI agent retrieval. It solves the problem of high latency and poor relevance in traditional RAG stacks by unifying retrieval, ranking, and learning into a single query. Core capabilities include personalized hybrid search, multi-stage ranking with ML models, and a feedback loop that improves results over time. The tool supports querying by text, user ID, or item ID, and integrates with over 30 native connectors. It is ideal for product and engineering teams building personalized feeds, search, recommendations, and AI assistants. Shaped claims to reduce agent costs from $1.50 to $0.03 per answer by eliminating retries and providing right-first-time results. It offers a SQL-like interface called ShapedQL and provides SDKs for Python, TypeScript, and MCP.
AI Tool Review Summary
4.7/5
High, consistent, and personalized
Clean and developer-friendly
LLMNLPMachine Learning
Real-time context engine for personalized retrieval and agent memory.
Integrates with 30+ data sources including Snowflake, BigQuery, Kafka, and Postgres.
Freemium with $100 free credits, then usage-based pricing
Features with the highest value for users are highlighted here.
ShapedQL unified query language
Hybrid semantic and keyword search
Personalized results per user
Feedback loop for continuous learning
Sub-50ms query latency
30+ native data connectors
Multi-stage ranking pipeline
Enterprise-grade security and compliance
Product teams
Engineering teams
AI/ML engineers
Data scientists
E-commerce companies
Media & streaming platforms
SaaS companies
Startups
Enterprise organizations
AI assistant developers
Free
$0/monthly
$100 free credits Up to 1M queries/month Community support
Popular
Pro
$99/monthly
10M queries/month Priority support Advanced analytics
Weekly intelligence on AI strategy, operations, and market shifts. No noise. No narrative. Direct to your inbox.
Pros & Cons
Dramatically reduces agent costs by providing accurate context on the first call. Unifies retrieval, ranking, and learning in a single query, simplifying the stack.
May require significant data integration effort to fully leverage personalization. Pricing details are not transparently listed on the website.
FAQs
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Rating
5.0
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