Qubrid AI, a pioneer in high-performance GPU Cloud solutions, announced the launch of its 2-Step No-Code Multimodal Retrieval-Augmented Generation (RAG) as a Service, a revolutionary way for businesses, researchers, and innovators to extract intelligence from their own data – instantly, securely, and without a single line of code.

Unlike generic chatbots and LLMs, which rely solely on static, pre-trained data and require advanced customization and prompting to handle proprietary files, Qubrid AI’s RAG-as-a-Service is built to:

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  • Chat directly with your own data – not just the model’s training set.
  • Handle multimodal inputs – documents, images, and small audio files in one seamless workflow.
  • Deliver source-backed answers – so you know exactly where the information came from.

“This is the future of AI interaction,” said Pranay Prakash, CEO of Qubrid AI. “Generic LLMs are powerful, but they’re not built to natively handle your files, verify its answers, or combine multiple data types in one conversation. We’ve solved that – while making RAG instant, no-code, and GPU-accelerated.”

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Breakthrough Features Include:

  • Upload & Chat – Drop in your files and start a conversation instantly.
  • Trusted Responses – Every answer comes with verifiable, clickable source citations.
  • Any File Type – Works with text documents, images, and small audio files seamlessly.
  • Smarter Organization (Coming Soon) – Keep multiple chat sessions and topic-specific collections.
  • Advanced Reading (Coming Soon) – Understands scanned, handwritten, and highly complex documents.
  • Model Switching (Coming Soon) – Switch to higher-accuracy models based on your needs and preferences.

Powered by Qubrid AI’s enterprise-grade GPU Cloud, the platform offers unmatched speed, scalability, and multimodal AI capability, making it ideal for industries like knowledge management, legal, healthcare, finance, research, and customer support – where accuracy, transparency, and proprietary data control are non-negotiable.

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Source – PR Newswire

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