troveGEN

troveGEN

Intellara Technologies
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About

Gemini Embedding models, including the newer Gemini Embedding 2, are part of Google’s Gemini AI ecosystem and are designed to convert text, phrases, sentences, and code into numerical vector representations that capture their semantic meaning. Unlike generative models that produce new content, the embedding model transforms input data into dense vectors that represent meaning in a mathematical format, allowing computers to compare and analyze information based on conceptual similarity rather than exact wording. These embeddings enable applications such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation pipelines. The model can process input in more than 100 languages and supports up to 2048 tokens per request, allowing it to embed longer pieces of text or code while maintaining strong contextual understanding.

About

troveGEN is a retrieval-augmented generation (RAG) and document-search platform for teams building AI products and organizations that need governed access to internal information. It ingests files, scanned documents, web pages and connected sources, then parses, OCRs, chunks, indexes and retrieves relevant passages with hybrid search, reranking, filters and access controls. Applications and AI agents can use its REST API, TypeScript SDK or Model Context Protocol server to search content and receive source-linked results; optional answer generation supports cited responses. Customers can use managed hosting or connect their own vector database, models and content sources, including air-gapped deployments. Use cases include internal knowledge search, customer support, technical documentation, and document workflows across finance, healthcare, legal and government.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

AI developers and data engineers who need a high-performance embedding model to convert text or code into semantic vectors for search, retrieval, and AI applications

Audience

AI product developers and organizations that need searchable, governed access to unstructured documents and knowledge, including engineering, HR, legal, healthcare, and financial-services teams.

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

$25/month
troveGEN-Z Launch costs $25/month and includes 1,500 pages, 8,000 searches, 1,500 answers, and two projects. Annual billing costs 10 times the monthly price. The one-time free trial includes 500 pages, 2,000 searches, and 200 answers. Sovereign deployments are quoted.
Free Version Not Supported
Free Trial Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Google
Founded: 1998
United States
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/

Company Information

Intellara Technologies
Founded: 2025
India
troveGEN.com

Alternatives

Alternatives

No Alternatives
txtai

txtai

NeuML

Categories

Embedding Models Supported

Categories

Integrations

Amazon S3 Not Supported
Azure Blob Storage Not Supported
Cohere Not Supported
Gemini Supported
Gemini Enterprise Supported
Gemini Enterprise Agent Platform Supported
Google AI Studio Supported
Google Drive Not Supported
Microsoft SharePoint Not Supported
Mimasa AI Supported
Model Context Protocol (MCP) Not Supported
OpenAI Not Supported
Pinecone Not Supported
Python Supported
Qdrant Not Supported
pgvector Not Supported

Integrations

Amazon S3 Supported
Azure Blob Storage Supported
Cohere Supported
Gemini Not Supported
Gemini Enterprise Not Supported
Gemini Enterprise Agent Platform Not Supported
Google AI Studio Not Supported
Google Drive Supported
Microsoft SharePoint Supported
Mimasa AI Not Supported
Model Context Protocol (MCP) Supported
OpenAI Supported
Pinecone Supported
Python Not Supported
Qdrant Supported
pgvector Supported
Claim Gemini Embedding 2 and update features and information
Claim Gemini Embedding 2 and update features and information
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Claim troveGEN and update features and information