Knowledge is scattered
Corporate knowledge is stored in files, folders and systems with no common preparation flow.
Anonymized production case
Knowledge preparation perimeter for RAG
A FOXOPS case around an internal RAG perimeter with knowledge buckets, documents, splitting, embeddings, vector search and controlled APIs.
Problem
Why RAG starts with knowledge preparation, not with chat
Documents must be split and indexed
Usable retrieval requires chunking, embeddings and controlled indexing.
Storage and retrieval are linked
The system must coordinate files, metadata and retrieval behavior.
AI quality depends on pipeline quality
Weak preparation produces weak search and weak answer generation.
Approach
How FOXOPS built this RAG perimeter
Approach 01
Knowledge buckets and documents
The system organized ingestion around explicit knowledge containers.
Approach 02
Splitting and embeddings
The pipeline used controlled splitting and embedding jobs to prepare retrieval units.
Approach 03
Vector search and API
The perimeter exposed a managed retrieval layer over corporate knowledge.
Solution perimeter
Buckets
knowledge containers
Documents
files / text
Splitter
chunks
Embeddings
vector layer
Search
RAG retrieval
Next Step
If you need an internal knowledge preparation perimeter for AI, this can be assessed separately
FOXOPS can help determine the right ingestion, indexing and retrieval model for a corporate RAG environment.