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Introduction:
Arches AI is a platform for exploring documents using artificial intelligence. It enables users to upload PDFs and interact with a personalized chatbot to ask questions and extract information. Key features include intelligent chatbot integration, on-premises deployment, AI-enhanced visual generation, and the ability to build semantic search engines. The platform supports multiple languages, voice-enabled chatbots, and deployment on private clouds or on-premise infrastructure.
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Arches AI Product Information
What is Arches AI?
Arches AI is a platform designed to explore documents using artificial intelligence. It allows users to upload PDFs and interact with a personalized chatbot to ask questions and extract information. Arches AI offers features such as intelligent chatbot integration, on-premises deployment, AI-enhanced visual generation, and the ability to create semantic search engines. It supports multiple languages, voice capabilities for chatbots, and can be deployed on private clouds or on-premise infrastructure.
How to use Arches AI?
Upload a PDF document to the Arches AI platform. Once uploaded, interact with the personalized chatbot to ask questions and extract insights from your document.
Arches AI's Core Features
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Intelligent Chatbot Integration
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On-Premises Deployment
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AI-Enhanced Visual Generation
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Semantic Search Engine Creation
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Document Summarization
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Voice-Enabled Chatbots
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Multi-Language Support
Arches AI Use Cases
#1
Creating semantic search engines for websites or applications.
#2
Embedding AI agents into existing websites.
#3
Analyzing and summarizing large volumes of documents.
#4
Deploying AI solutions on private cloud or on-premise infrastructure.
FAQ from Arches AI
How does Arches AI work? +
Arches AI uses machine learning to convert documents into 'word embeddings.' These embeddings enable semantic searches based on meaning rather than exact keyword matches, which is particularly useful for analyzing unstructured text like textbooks, articles, and documentation.
What are the main features of Arches AI? +
Arches AI provides several features, including semantic document search, multi-file searching, an API for platform integration, and the ability to embed interactive widgets directly into your website or application.
Can I integrate Arches AI with my application? +
Yes. Arches AI provides API tokens that allow developers to integrate its search capabilities into other platforms, applications, or websites by making authenticated requests.
How secure is my data with Arches AI? +
Arches AI prioritizes data security by storing documents in encrypted cloud storage with strict protocols to prevent unauthorized access. Your data is protected, and you can delete any documents at any time via the 'Files' page.
How are credits calculated in Arches AI? +
Credits are based on token usage: - Upload: 1 credit per 50 tokens (e.g., a 1000-word document costs 20 credits). - Chat: 1 credit per 1 token (e.g., a query with 1000 words of context costs 1000 credits). - Image: 500 credits per image processed. - Animation: 85 credits per frame (e.g., a 10-second video at 12fps costs 10,200 credits).
Arches AI Pricing
Related Model Comparison Pages
Use these comparison pages to understand the trade-offs between the models most relevant to Arches AI.
Compare Gemini 1.0 Pro Deprecated and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 1.0 Pro Deprecated and Gemini 2.5 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.0 Flash Lite and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.5 Flash and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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