back_office_ops · saas · workflow
CoSpaceGPT replaces in-house RAG with Needle for accurate multi-file retrieval
CoSpaceGPT needed a robust, highly accurate RAG pipeline to retrieve context across mixed file types and large multi-file workspaces, but their in-house similarity-search approach broke under scale and could not maintain accuracy.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · File upload to workspace
Users continuously upload files inside chat threads within long-term project workspaces.
Tools used
Needle
Outcome
CoSpaceGPT found Needle easy to integrate with minimal engineering effort, achieving good retrieval accuracy for their multi-file use case and allowing the team to focus on product improvements rather than infrastructure complexity.
What failed first
CoSpaceGPT's internally built RAG system relied on similarity search over embedded chunks. It worked initially but broke as file volumes grew, failing to handle mixed media and multi-file queries.
Results
Volumepretty good accuracy
Grounding & classification
Source type: vendor customer story
15 fields verified against source quotes.
document aiknowledge searchragknowledge basefailure mode describednamed customertools describedworkflow describedsoftwareaccuracy improvementvendor customer storyback office opsrag answering