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Average Ratings 0 Ratings

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features
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support

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Description

Accurez serves as a private, self-hosted AI knowledge base tailored for business teams seeking immediate and reliable responses derived from their internal documents. Operating on your own infrastructure, it utilizes Docker along with Postgres, Qdrant, and Redis for seamless integration. Users can select their preferred LLM provider, whether by utilizing OpenAI-compatible APIs or deploying a local model through Ollama for those requiring air-gapped solutions. Each response is accompanied by the original source document, featuring chunk-level excerpts and a confidence rating classified as High, Moderate, or Low. To ensure accuracy, grounding validation is implemented to minimize the risk of unverified information reaching your team. Notable characteristics include private self-hosted deployment, source citations, confidence ratings, a hybrid semantic search system combining BM25 and vector techniques, scoped AI assistants, analytics for coverage, support for multi-source ingestion from formats such as PDF, Markdown, Google Drive, Notion, and URLs, as well as an embeddable widget and a public help center. Additionally, it supports local AI capabilities via Ollama, audit logging, and customizable platform branding. This solution requires a one-time payment, eliminating the need for subscriptions or per-seat fees, and has been developed by RadicalStart since 2016, reflecting a commitment to evolving business needs.

Description

Experience the power of localized and secure AI right on your desktop, providing you with in-depth insights while maintaining complete data security and privacy. Our innovative macOS-native application combines efficiency, privacy, and intelligence through its state-of-the-art AI functionalities. The RAG system is capable of tapping into data from a local knowledge base to enhance the capabilities of the large language model (LLM), allowing you to keep sensitive information on-site while improving the quality of responses generated by the model. To set up RAG locally, you begin by breaking down documents into smaller segments, encoding these segments into vectors, and storing them in a vector database for future use. This vectorized information will play a crucial role during retrieval operations. When a user submits a query, the system fetches the most pertinent segments from the local knowledge base, combining them with the original query to formulate an accurate response using the LLM. Additionally, we are pleased to offer individual users lifetime free access to our application. By prioritizing user privacy and data security, our solution stands out in a crowded market.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

No images available

Screenshots View All

Integrations

Codestral No 
Codestral Mamba No 
Hugging Face No 
LangChain No 
Llama No 
Llama 2 No 
Llama 3 No 
Llama 3.1 No 
Llama 3.2 No 
Mathstral No 
Ministral 3B No 
Mistral 7B No 
Mistral AI No 
Mistral NeMo No 
Mistral Small No 
Mixtral 8x22B No 
Mixtral 8x7B No 
Pixtral Large No 
Swift No 

Integrations

Codestral Yes 
Codestral Mamba Yes 
Hugging Face Yes 
LangChain Yes 
Llama Yes 
Llama 2 Yes 
Llama 3 Yes 
Llama 3.1 Yes 
Llama 3.2 Yes 
Mathstral Yes 
Ministral 3B Yes 
Mistral 7B Yes 
Mistral AI Yes 
Mistral NeMo Yes 
Mistral Small Yes 
Mixtral 8x22B Yes 
Mixtral 8x7B Yes 
Pixtral Large Yes 
Swift Yes 

Pricing Details

$0
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac Yes 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Accurez

Founded

2026

Country

United States

Website

www.accurez.ai/

Vendor Details

Company Name

Klee

Website

kleedesktop.com

Product Features

Product Features

Artificial Intelligence

Chatbot No 
For Healthcare No 
For Sales No 
For eCommerce No 
Image Recognition No 
Machine Learning No 
Multi-Language No 
Natural Language Processing No 
Predictive Analytics No 
Process/Workflow Automation No 
Rules-Based Automation No 
Virtual Personal Assistant (VPA) No 

Alternatives

Alternatives

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