Shaped

Shaped is a real-time context engine for agentic AI, search and recommendations. Retrieve, rank and personalize results with user, item and interaction context.

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What Shaped does

Shaped is a real-time context engine for agentic AI, search, and recommendations. It combines retrieval, ranking, embeddings, and learning so applications can return relevant results based on user, item, and query context instead of relying on static vector search alone.

The site positions Shaped as a managed alternative to building a retrieval stack from separate databases, ranking systems, connectors, and pipelines. It is used for personalized search, product recommendations, agent memory and retrieval, and other ranking-driven experiences where relevance needs to improve from interaction data.

Core capabilities

Unified query layer

Compose retrieval, ranking, filtering, scoring, and reordering in a single query through ShapedQL, rather than stitching together separate services.

Hybrid search and ranking

Blend semantic search, keyword search, and behavioral ranking so results can match intent while still honoring exact matches and business rules.

Context-aware personalization

Use user, item, or text context to personalize outputs, including recommendations and agent retrieval that adapt to who is asking.

Native data connectors

Connect batch and streaming sources through native connectors so data freshness and event signals can flow into the model.

Learning from interactions

Train and update ranking models from interaction signals, with the site describing continuous learning and a feedback loop.

Multiple integration paths

Access the product through Python SDK, TypeScript SDK, or MCP, alongside the query interface shown on the site.

Where Shaped fits

Personalized product recommendations
For e-commerce teams, Shaped can personalize home pages, product detail pages, carts, email, and post-purchase surfaces using catalog metadata and user behavior.
Hybrid search
For product search, it combines semantic and keyword signals so teams can improve relevance, reduce zero-result queries, and still respect exact-match behavior.
Agent retrieval and memory
For agentic applications, it can retrieve context by text, user ID, or item ID so assistants can ground responses in the right data.
Personalized ranking across surfaces
For content and commerce experiences, it supports feeds, similar-items ranking, category ranking, and item reranking across surfaces.
Rapid experimentation
For teams that need to iterate quickly, it provides a managed workflow for connecting data, tuning relevance, and shipping experiments without assembling infrastructure first.

Pros and Cons

Pros

Combines retrieval, ranking, and learning in one product instead of requiring a separate stack of tools.
Supports multiple use cases, including search, recommendations, agent retrieval, feeds, and item reranking.
Offers native connectors and both batch and real-time ingestion options for bringing data into the system.
Provides a SQL-like query layer and SDK options for teams that want to integrate programmatically.
Includes a free start option and clear paid tiers for teams that want to evaluate the product before buying.

Cons

The public materials are broad on low-level implementation details such as exact onboarding steps, output schemas, and deployment constraints.
Some plan details are limited to pricing-page summaries, so readers may need to contact sales for enterprise-specific terms or custom contracts.

FAQ

How do you get started with Shaped?

Shaped is set up by connecting your data, then configuring a feed or query in its interface. The source materials describe a three-step workflow: connect data, configure the feed or model, and deploy automatically.

What kinds of workflows does Shaped support?

The product supports retrieval and ranking workflows for search, recommendations, agent retrieval, and other personalized surfaces. The source also shows it can be used through a SQL-like query layer and via Python SDK, TypeScript SDK, or MCP.

Does Shaped offer a free tier or trial?

Pricing pages show a free start option with $100 included and no credit card required, plus paid Starter, Standard, and Enterprise plans. Standard is described as usage-based and Enterprise adds custom contracts and dedicated support.

What support options are available?

The public materials mention support during business hours for Standard, and 24/7 support plus a private Slack or email channel for Enterprise. Starter includes Docs + Slack support.