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Comprehensive 文書処理ソリューション Tools for Every Need

Get access to 文書処理ソリューション solutions that address multiple requirements. One-stop resources for streamlined workflows.

文書処理ソリューション

  • Velatir

    Velatir enhances business operations with intelligent AI-driven document automation.
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    What is Velatir?
    Velatir is an AI-powered solution that specializes in automating document workflows. It enables users to effortlessly extract, analyze, and manage data from various document formats. The AI Agent enhances productivity by reducing manual efforts and minimizing errors, thus allowing businesses to focus on strategic activities.
    Velatir Core Features
    • Document automation
    • Data extraction
    • Workflow management
    • Integration with existing systems
    Velatir Pro & Cons

    The Cons

    No information on open-source availability.
    No details about pricing tiers or cost after free trial.
    No mobile app or browser extension availability indicated.
    No public GitHub repository for community contributions or transparency.

    The Pros

    Provides seamless human oversight and control over AI agents.
    Compatible with all major LLMs and AI frameworks.
    Built-in compliance aligned with EU AI Act and other regulations.
    Customizable decision flows tailored to business needs.
    Real-time monitoring and notification integration with existing tools.
    Risk scoring engine enhances decision prioritization and efficiency.
    Easy and fast integration with AI models.
    Velatir Pricing
    Has free planNo
    Free trial details30-day free trial with all features included in the business plan
    Pricing modelFree Trial
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://www.velatir.com/pricing

  • Controllable RAG Agent

    An open-source Python framework to build Retrieval-Augmented Generation agents with customizable control over retrieval and response generation.
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    What is Controllable RAG Agent?
    The Controllable RAG Agent framework provides a modular approach to building Retrieval-Augmented Generation systems. It allows you to configure and chain retrieval components, memory modules, and generation strategies. Developers can plug in different LLMs, vector databases, and policy controllers to adjust how documents are fetched and processed before generation. Built on Python, it includes utilities for indexing, querying, conversation history tracking, and action-based control flows, making it ideal for chatbots, knowledge assistants, and research tools.
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