Wetrocloud

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  Wetrocloud Website

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  RAG with Wetrocloud is amazing

We were tired of seeing developers and startups struggle to query LLMs with their custom data. Everyone wanted to build with LLMs, but when it came to querying their custom data from documents, images, videos, web urls and databases, the process became incredibly hard. Between setting up vector databases, handling embeddings, building semantic search, and integrating large language models, most teams give up or waste time rebuilding the same pipeline. So we decided to fix it.

Wetrocloud is a plug-and-play Retrieval-Augmented Generation (RAG) platform. It lets developers query any of their custom data, from documents, web pages, audio, or YouTube videos, with any LLM of their choice (GPT-4o, Claude 3.7, DeepSeek R1, etc.). We support everything from data extraction to parsing, chunking, embedding, indexing, retrieval, and generation, so you don’t have to stitch multiple tools together. Just bring your data, choose your LLM, and get to work.

We started by building the core infrastructure, a RAG pipeline that handles unstructured and structured content.

• Set up a fully managed vector database with hybrid semantic search
• Built our own API layer with support for text, audio, image, and video resources
• Created a real-time token pricing engine for usage tracking
• Integrated support for all major models like GPT, Claude, DeepSeek, Gemini, and more
• Designed a developer playground, SDKs, and docs to make it easy to try, build, and scale

Getting RAG to work consistently across different file types from documents, images, audio and video was tough. We had to build a scalable infrastructure that supported low-latency vector search and dynamic LLM selection, then create a clean developer documentation that made it easy for anyone to use without AI expertise while making it flexible enough for startups and powerful enough for enterprises.

So far we've amassed over 100 developers signed up within our first few months. Our work was featured in top AI developer communities and got adopted by startup teams building in EdTech, LegalTech, and more.

Developers want simple AI infrastructure, not another framework to learn. The real challenge isn’t LLMs, it’s connecting LLMs to the data in a way that’s fast, accurate, and scalable with great docs, fast response time, and working examples while having a pricing that is as flexible as the API itself.

Our next goal is in growing our ecosystem of tutorials, dev videos, and open-source examples to help dev teams build RAG applications/systems faster.

Built With

• embedding-models
• llms
• loaders
• vector-databases