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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Agent Search on Gemini Enterprise Agent Platform is an advanced search solution that brings Google-level search capabilities to enterprise data and applications. It allows developers to create intelligent search experiences for websites and internal systems using both structured and unstructured data. By incorporating generative AI, the platform replaces basic keyword matching with conversational and context-aware search results. It functions as a ready-to-use retrieval augmented generation (RAG) system, grounding AI responses in enterprise data for improved accuracy. The platform simplifies complex backend processes such as ETL, indexing, and embedding generation, reducing development time significantly. It offers industry-specific solutions for sectors like healthcare, media, and retail, enabling more personalized and relevant search experiences. Developers can also build custom solutions using APIs for vector search, document parsing, and ranking. The integration with vector databases allows for advanced semantic search and recommendation systems. With minimal setup, users can deploy search engines directly into websites or applications. Continuous refinement tools help optimize search performance and relevance. Overall, it empowers businesses to deliver faster, smarter, and more engaging search experiences powered by generative AI.

Description

Vectara offers LLM-powered search as-a-service. The platform offers a complete ML search process, from extraction and indexing to retrieval and re-ranking as well as calibration. API-addressable for every element of the platform. Developers can embed the most advanced NLP model for site and app search in minutes. Vectara automatically extracts text form PDF and Office to JSON HTML XML CommonMark, and many other formats. Use cutting-edge zero-shot models that use deep neural networks to understand language to encode at scale. Segment data into any number indexes that store vector encodings optimized to low latency and high recall. Use cutting-edge, zero shot neural network models to recall candidate results from millions upon millions of documents. Cross-attentional neural networks can increase the precision of retrieved answers. They can merge and reorder results. Focus on the likelihood that the retrieved answer is a probable answer to your query.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Chat Mitra
Datavolo
Gemini
Gemini 2.5 Flash Native Audio
Gemini Enterprise
Gemini Enterprise Agent Platform
Google Cloud Document AI
Google Cloud Platform
HTML
IBM watsonx.data
Langflow

Integrations

Chat Mitra
Datavolo
Gemini
Gemini 2.5 Flash Native Audio
Gemini Enterprise
Gemini Enterprise Agent Platform
Google Cloud Document AI
Google Cloud Platform
HTML
IBM watsonx.data
Langflow

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

Google

Founded

1998

Country

United States

Website

cloud.google.com/products/gemini-enterprise-agent-platform/agent-search

Vendor Details

Company Name

Vectara

Founded

2020

Country

United States

Website

vectara.com

Product Features

Enterprise Search

AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery

Product Features

Enterprise Search

AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery

Alternatives

Alternatives

Semantee Reviews

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