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What is Meii AI?

Meii AI is at the leading edge of AI advancements, offering specialized Large Language Models that can be tailored with organizational data and securely hosted in either private or cloud environments. Our approach to AI, grounded in Retrieval Augmented Generation (RAG), seamlessly combines Embedded Models and Semantic Search to provide customized and insightful responses to conversational queries, specifically addressing the needs of enterprises. Drawing from our unique expertise and over a decade of experience in Data Analytics, we integrate LLMs with Machine Learning algorithms to create outstanding solutions aimed at mid-sized businesses. We foresee a future where individuals, companies, and government bodies can easily harness the power of advanced technology. Our unwavering commitment to making AI accessible for all motivates our team to persistently break down the barriers that hinder machine-human interaction, thereby cultivating a more interconnected and efficient global community. This vision not only highlights our dedication to innovation but also emphasizes the transformative impact of AI across various industries, enhancing productivity and fostering collaboration. Ultimately, we believe that our efforts will lead to a significant shift in how technology is perceived and utilized in everyday life.

What is Gemini Embedding?

The first text model of the Gemini Embedding, referred to as gemini-embedding-001, has officially launched and is accessible through both the Gemini API and Gemini Enterprise Agent Platform, having consistently held its top spot on the Massive Text Embedding Benchmark Multilingual leaderboard since its initial trial in March, thanks to its exceptional performance in retrieval, classification, and multiple embedding tasks, outperforming both legacy Google models and those from other external developers. Notably, this versatile model supports over 100 languages and features a maximum input limit of 2,048 tokens, employing the cutting-edge Matryoshka Representation Learning (MRL) technique, which enables developers to choose from output dimensions of 3072, 1536, or 768 for optimal quality, efficiency, and performance. Users can easily access this model through the well-known embed_content endpoint in the Gemini API. This transition process is designed for a smooth user experience, minimizing any impact on existing workflows and ensuring continuity in operations. The launch of this model represents a significant step forward in the field of text embeddings, paving the way for even more advancements in multilingual applications.

Media

No images available

Media

Integrations Supported

Integrations Supported

Gemini
Gemini Enterprise
Gemini platform
Google AI Studio
Mimasa AI
Python

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

$0.15 per 1M input tokens

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Meii AI

Date Founded

2024

Company Location

India

Company Website

www.meii.ai/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Categories and Features

Agentic AI

Not specified

AI Agents

Not specified

AI Automation

Not specified

Embedding Models

Not specified

Workflow Automation

Not specified

Categories and Features

Embedding Models

Not specified

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