By AdeB Olumide AdeB Olumide
on AI Developer Tools

Shaped

AI-native vector database with a feedback loop

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What Is Shaped?

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Shaped is an AI-native vector database built for decisions rather than static documents. It unifies the query, intelligence, and data layers so teams can run agents, personalized search, and recommendations from a single platform.

Shaped manages embeddings, data ingestion, model training, and feedback, allowing teams to focus on user-facing features instead of infrastructure. By combining behavioral signals, content, and real-time context, it continuously optimizes what each user sees next.

Leading marketplaces, media platforms, and consumer apps use Shaped to improve engagement, retention, bookings, and revenue.

Quick Snapshot

Shaped combines a vector database with an integrated feedback and intelligence layer to turn behavioral and content data into production-ready AI decisions. Teams can ship high-performing recommendations, search, and agent experiences in days instead of months.

Works on
  • Web
  • API
Pricing Model
Cannot determine the price.
Fits on
Affiliate Program
We could not identify an affiliate program.
API Availability
Shaped has an API available.
Key Features
  1. Unify search, recommendations, and agents
  2. Leverage real-time behavioral and freshness signals
  3. Continuously improve relevance with feedback
Audience
  • developers
  • data scientists
  • product managers
  • machine learning engineers
  • AI teams
  • marketplaces
  • media platforms
  • e-commerce companies
  • consumer app teams
URL
https://shaped.ai

Screenshot

Shaped

Key Features of Shaped

AI-native vector database

Stores and queries embeddings to power similarity search and relevance-driven experiences across agents, search, and recommendations.

Real-time retrieval engine

Returns highly relevant results in milliseconds, enabling responsive search, feeds, and agent interactions at production scale.

Built-in feedback loop

Captures user behavior and feedback to continuously refine and improve relevance models over time.

Unified relevance engine

Connects query, intelligence, and data layers so teams can manage search, recommendations, and agents from a single platform.

Embeddings and data ingestion

Handles ingestion of embeddings, content, and behavioral signals so teams can focus on building product experiences instead of plumbing.

Model training and optimization

Provides built-in capabilities for training and tuning relevance models using real-world feedback and signals.

Use Cases for Shaped

Personalized search

Deliver highly relevant, real-time search results by combining embeddings, behavioral data, and freshness signals in a single relevance engine.

Content recommendations

Recommend the next best item, article, or video by leveraging user behavior and content signals to drive engagement and retention.

AI agents retrieval

Power AI agents with fast, context-aware retrieval so they can surface the most relevant information or actions in milliseconds.

Marketplace ranking

Optimize marketplace listings and rankings using real-time feedback and behavioral signals to improve bookings and revenue.

Media & feed ranking

Rank feeds and media streams based on user interest and freshness to keep users engaged with continuously updated recommendations.

Frequently Asked Questions

What is Shaped and what does it do?

Shaped is an AI-native vector database and real-time retrieval engine with a built-in feedback loop. It powers AI agents, personalized search, and recommendations by connecting your data, embeddings, models, and freshness signals to deliver highly relevant results.

How is Shaped different from a traditional vector database?

Unlike traditional vector databases that focus mainly on storage and retrieval, Shaped integrates an intelligence and feedback layer. It manages embeddings, data ingestion, model training, and real-time feedback so you can build full relevance systems rather than just raw similarity search.

What types of use cases is Shaped best suited for?

Shaped is designed for relevance-critical experiences such as personalized search, recommendations, agent retrieval, marketplace ranking, and media feed ranking. It is particularly useful when engagement, retention, bookings, or revenue depend on showing the right content to each user.

Who should use Shaped?

Shaped is aimed at developers, data scientists, product managers, machine learning engineers, and AI teams building marketplaces, media platforms, e-commerce products, and other consumer apps that rely on high-quality recommendations and search.

Does Shaped offer an API?

Yes, Shaped provides an API that allows teams to integrate its vector database and retrieval engine into their applications for agents, search, and recommendations.

How does Shaped improve relevance over time?

Shaped includes a built-in feedback loop that captures user behavior and interactions. This feedback is used to continuously update and optimize models so that search results, recommendations, and agent responses become more relevant over time.

What are the pricing options for Shaped?

Pricing information is not publicly available in the provided context and likely requires contacting Shaped directly or accessing its console.

Shaped · Our Verdict

Shaped stands out by tightly coupling a vector database with an intelligence and feedback layer, which many infrastructure tools leave to users to assemble themselves. For teams building relevance-critical experiences like recommendations and search, this integration can significantly reduce engineering overhead and time-to-market while still remaining flexible enough for complex, data-rich products.

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