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Description

Keenable operates as a standalone web search infrastructure tailored for AI laboratories, inference frameworks, agents, and developers seeking quick and reliable access to real-time web content. The Search API equips AI entities with an extensive index comprising over 100 billion documents, specifically designed for rapid retrieval with performance fine-tuned for demanding production agent tasks. Agents are enabled to search through web pages and obtain page content via a REST API, MCP server, or command-line interface, all under a single account and API key. Continuously striving for excellence, Keenable assesses and enhances search quality through its NEEDLE benchmark, which evaluates retrieval efficiency across various search providers and aligns results with an oracle ranking derived from aggregated outcomes. For expansive AI tasks, the platform offers dedicated search capacity alongside options for cloud and on-premises deployment. Additionally, its Time Machine feature enhances retrieval capabilities by allowing users to conduct searches across historical webpage versions, offering a comprehensive view of past content. This dual focus on current and historical data positions Keenable as a versatile tool for modern AI applications.

Description

Oracle AI Vector Search is an innovative feature integrated into Oracle Database, specifically tailored for AI applications, which enables the querying of data based on its semantic meaning rather than relying solely on conventional keyword searches. This functionality empowers organizations to conduct similarity searches across both structured and unstructured datasets, allowing for retrieval of results that prioritize contextual relevance over precise matches. Employing vector embeddings to represent various forms of data—including text, images, and documents—it utilizes advanced vector indexing and distance metrics to quickly locate similar items. Moreover, it introduces a unique VECTOR data type along with SQL operators and syntax that enable developers to merge semantic searches with relational queries within a single database framework. As a result, this integration streamlines the data management process by negating the necessity for separate vector databases, ultimately minimizing data fragmentation and fostering a cohesive environment for both AI and operational data. The enhanced capability not only simplifies the architecture but also enhances the overall efficiency of data retrieval and analysis in complex AI workloads.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Claude
Claude Code
Claude Desktop
Convex
Haystack
Hermes Agent
Hugging Face
Ketch
LangChain
Lightpanda
LlamaIndex
Model Context Protocol (MCP)
My DSO Manager
NVIDIA NeMo
OpenCode
Oracle Database
Pi
RAGFlow
SQL
Visual Studio Code

Integrations

Claude
Claude Code
Claude Desktop
Convex
Haystack
Hermes Agent
Hugging Face
Ketch
LangChain
Lightpanda
LlamaIndex
Model Context Protocol (MCP)
My DSO Manager
NVIDIA NeMo
OpenCode
Oracle Database
Pi
RAGFlow
SQL
Visual Studio Code

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Keenable

Founded

2026

Country

United States

Website

keenable.ai/

Vendor Details

Company Name

Oracle

Country

United States

Website

www.oracle.com/database/ai-vector-search/

Product Features

Alternatives

Alternatives