Description
Shaped is a powerful real-time context engine designed to enhance agentic AI by providing precise and relevant information. It addresses the common challenge of information overload by filtering vast datasets down to the most pertinent results, ensuring AI agents receive actionable context. This leads to more efficient and accurate AI performance, reducing the need for extensive manual filtering or costly retry loops.
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At its core, Shaped functions as a unified relevance engine, integrating retrieval, ranking, and learning capabilities into a single queryable system. Unlike traditional RAG stacks that often require multiple integrations and significant engineering effort, Shaped streamlines the process. It connects to over 30 data sources, supporting both batch and real-time data streams, and unifies them into a single schema. This allows for hybrid search that blends semantic and keyword relevance, reranked by user behavior and business rules.
The engine operates on a three-layer architecture: the Query layer for real-time retrieval and ranking with sub-50ms latency; the Intelligence layer for ML models, embeddings, and continuous learning; and the Data layer for seamless data ingestion from various sources. ShapedQL, its SQL-like interface, enables retrieval by text, user ID, or item ID, incorporating hard constraints, business rules, and ML model scoring.
Shaped is particularly beneficial for product and engineering teams looking to improve engagement and revenue. Its capabilities extend to personalized content feeds, hybrid search and discovery, agent retrieval for contextual memory, similar item recommendations, personalized email content, and AI assistants. The platform boasts enterprise-grade security, including SOC 2 Type II certification and GDPR/HIPAA compliance, alongside a 99.95% uptime SLA, making it suitable for large-scale deployments.
Key advantages include a significant cost reduction compared to DIY solutions, with costs as low as $0.03 per answer. The feedback loop mechanism ensures that results continuously improve with every user interaction, making the AI more intelligent over time. Shaped aims to replace entire retrieval stacks, offering a more efficient, cost-effective, and performant alternative for building intelligent applications.
- CategoryAI Search Engines
- Pricing ModelPay-per-answer
- DeploymentCloud
- PlatformsWeb
Shaped Real-Time Context Engine's Core Features
Real-time context engine for AI agents
Delivers top 10 relevant results
Cost-effective at $0.03 per answer
Powers personalized search and recommendations
Unified retrieval, ranking, and learning
Hybrid search (semantic and keyword)
Personalized results per user
Continuous learning via feedback loop
30+ native data connectors
SOC 2 Type II certified
GDPR/HIPAA compliant
99.95% uptime SLA
ShapedQL interface for context retrieval
Sub-50ms latency for retrieval and ranking
How to use Shaped Real-Time Context Engine?
Connect Data: Integrate your data sources via batch or real-time streams using 30+ native connectors.
Build Model: Develop and iterate on ranking models using Shaped's intelligence layer.
Configure Retrieval: Define retrieval strategies using ShapedQL, specifying context and constraints.
Deploy Agent: Integrate Shaped into your AI agent for real-time contextual memory and retrieval.
Monitor Performance: Track accuracy, hit rates, and ranking relevance.
Optimize: Leverage the feedback loop to continuously improve results based on user interactions.
Shaped Real-Time Context Engine's Use Cases
- Agent Retrieval
- Personalized Search
- Recommendations
- E-commerce Search
- Personalized Feeds
- AI Assistants
- Document Retrieval
