• Simply Automation with Gen AI Services

     

     

    Empower Your Workforce with Automated Workflow

  • Unleash the Power of Domain-Specific RAG Services

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    RAG Foundation Services

    • Fine-tuned Embedding Models:
      Fine-tunes embedding models with your domain-specific dataand domain language and terminology, it keeps RAG process becomes significantly more accurate and relevant to your specific needs.
    • Fine-tuned LLMs and SLMs: Fine-tunes both LLMs and SLMs with your domain-specific knowledge. This creates industry-specific AI assistants that understand your industry's language and terminology. More accurate results and insightful automation tailored to your unique workflows.
    • RAG Data Curation Pipeline:
      RAG as a Service incorporates a comprehensive data curation pipeline, which ensures high-quality data is retrieved, augmented, and ultimately used for generation.
    • Seamless Workflow Integration: Our solution triggers existing workflows based on specific triggers and function calling, allowing RAG Serivce to automate tasks seamlessly within your established processes.

     

     

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    RAG as a Service Deployment Choices

    Cloud-Based RAG as a Service:

    • Effortless Scalability: Benefit from the virtually unlimited resources of the cloud to scale your AI applications effortlessly as your needs evolve.
    • Reduced On-Premise Infrastructure: Leverage the cloud-based infrastructure, eliminating the need for expensive hardware investments and ongoing maintenance.
    • Simplified Deployment: Get started quickly and easily with our cloud-based solution and allowing you to focus on integrating AI into your workflows.

    On-Premise Local RAG as a Service:

    • Enhanced Security & Control: Maintain complete control over your data and models with an on-premise solution. Ideal for industries with strict data privacy regulations.
    • Reduced Reliance on External Infrastructure: Process sensitive data on-site, minimizing reliance on external cloud services.
    • Customization Flexibility: Enjoy greater control over model training and customization to perfectly align with your unique domain-specific needs.
    broken image

    RAG as a Service Deployment Choices

    Cloud-Based RAG as a Service:

    • Effortless Scalability: Benefit from the virtually unlimited resources of the cloud to scale your AI applications effortlessly as your needs evolve.
    • Reduced On-Premise Infrastructure: Leverage the cloud-based infrastructure, eliminating the need for expensive hardware investments and ongoing maintenance.
    • Simplified Deployment: Get started quickly and easily with our cloud-based solution and allowing you to focus on integrating AI into your workflows.

    On-Premise Local RAG as a Service:

    • Enhanced Security & Control: Maintain complete control over your data and models with an on-premise solution. Ideal for industries with strict data privacy regulations.
    • Reduced Reliance on External Infrastructure: Process sensitive data on-site, minimizing reliance on external cloud services.
    • Customization Flexibility: Enjoy greater control over model training and customization to perfectly align with your unique domain-specific needs.
  • Use Cases

     

  • About PyligentAI

    Empowering Businesses with Industry-Specific AI Solutions

     

    PyligentAI is a pioneering force in the field of GenAI, dedicated to unlocking the true potential of enterprise data. We develop cutting-edge AI solutions that transform the way businesses access and utilize knowledge, driving innovation and efficiency across various industries. Please refer our keynote speeches for deeper insights into the indutry. [Link]

     

     

     

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  • Blogs

    December 10, 2024
    A Practical Examination of LLMs in Finance Industry at 5th Annual Machine Learning in...
    Read more...
    RAG (Retrieval-Augmented Generation) technology holds immense potential for revolutionizing how...
    Read more...
    Key Summary 1 Environment Setup Train Evironment : NIVIDA A100Framework: Huggingface,...
    Read more...
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