How CloudsineAI built a robust, highly accurate RAG pipeline with Needle

CloudsineAI builds secure, enterprise-grade AI applications. Their product CoSpaceGPT is a collaborative GenAI workspace where teams upload files, ask questions, and generate documents or presentations using multiple LLMs.

Accurate document understanding is core to the experience — users frequently upload PDFs, PPTs, diagrams, and images and expect reliable, context-aware answers.

From the very beginning, the CoSpaceGPT team knew they needed robust, highly accurate RAG pipeline capable of retrieving context across many different file types, including:

• PDFs with heavy visuals
• Diagrams and images
• PowerPoints and Word documents
• Large multi-file project workspaces

They first attempted to build this internally, but the limitations became clear as usage grew. As Sanat explained:

"We wanted to have our own RAG system because our use case is very much about accuracy of retrieval from files. We actually tried building our own one first. It worked for some time, but it quickly broke when there were a lot of files involved… we were just doing similarity search over embedded chunks."

With increasing file volumes, mixed media (PDFs, diagrams, images), and multi-file queries, their in-house approach couldn't scale or maintain accuracy. They also required:

• Stable, isolated context boundaries per chat thread
• Long-term knowledge bases for ongoing projects
• A way to handle continuous file uploads from users inside those chats

That's when they began evaluating external providers and their requirements led them to Needle.

"We then sought to find a provider which could do this for us. And yeah, we found Needle — I think it was easy to integrate and it gave us pretty good accuracy."

Sanat noted that the integration required minimal engineering effort, and this ease allowed the team to focus on product improvements rather than infrastructure complexity.

Needle became the backend powering document retrieval in CoSpaceGPT:

• Each chat thread maps cleanly to a single Needle collection
• Project spaces map to long-lived Needle collections
• Searches pull from both the thread and the project for richer answers

This architecture matched Cloudsine's product needs perfectly.

A. Accurate retrieval across diverse file types

CoSpaceGPT users upload PDFs, images, diagrams, PPTs and more — and Needle reliably indexes them and returns the correct context chunks, improving the output quality of their LLM workflows. Cloudsine specifically enjoys the accuracy as the key reason they chose Needle.

B. Scales with user behavior and real-world usage

Their design — "one collection per thread" — generates many concurrent collections. Needle's system supports this pattern and keeps retrieval performance high as usage grows.

C. Cost-efficient and aligned with Cloudsine's usage patterns

Needle API's pay-as-you-go pricing model aligns well with Cloudsine's variable workload. Since they index files only when users upload them and access the API as needed, the consumption-based model keeps costs predictable and efficient — especially compared to alternatives that require fixed monthly infrastructure commitments.

If your product relies on dependable document understanding or needs to scale retrieval across thousands of user uploads, Needle can power that foundation for you too. Try Needle for your next AI product and see how quickly you can deliver accurate, production-ready retrieval.