troveGEN

troveGEN

Intellara Technologies
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About

MonoQwen2-VL-v0.1 is the first visual document reranker designed to enhance the quality of retrieved visual documents in Retrieval-Augmented Generation (RAG) pipelines. Traditional RAG approaches rely on converting documents into text using Optical Character Recognition (OCR), which can be time-consuming and may result in loss of information, especially for non-textual elements like graphs and tables. MonoQwen2-VL-v0.1 addresses these limitations by leveraging Visual Language Models (VLMs) that process images directly, eliminating the need for OCR and preserving the integrity of visual content. This reranker operates in a two-stage pipeline, initially, it uses separate encoding to generate a pool of candidate documents, followed by a cross-encoding model that reranks these candidates based on their relevance to the query. By training a Low-Rank Adaptation (LoRA) on top of the Qwen2-VL-2B-Instruct model, MonoQwen2-VL-v0.1 achieves high performance without significant memory overhead.

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

Companies working with visually rich documents requiring a solution to enhance retrieval accuracy and efficiency in OCR-free RAG systems

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 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

No information available.
Free Version Not 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 Supported
Live Online Supported
In Person Supported

Training

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

Company Information

LightOn
Founded: 2016
France
www.lighton.ai/lighton-blogs/monoqwen-vision

Company Information

Intellara Technologies
Founded: 2025
India
troveGEN.com

Alternatives

RankLLM

RankLLM

Castorini

Alternatives

No Alternatives
RankGPT

RankGPT

Weiwei Sun

Categories

Reranking Models Supported

Categories

Integrations

Amazon S3 Not Supported
Azure Blob Storage Not Supported
Cohere Not Supported
Google Drive Not Supported
Microsoft SharePoint Not Supported
Model Context Protocol (MCP) Not Supported
OpenAI Not Supported
Pinecone Not Supported
Qdrant Not Supported
pgvector Not Supported

Integrations

Amazon S3 Supported
Azure Blob Storage Supported
Cohere Supported
Google Drive Supported
Microsoft SharePoint Supported
Model Context Protocol (MCP) Supported
OpenAI Supported
Pinecone Supported
Qdrant Supported
pgvector Supported
Claim MonoQwen-Vision and update features and information
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