RAG Pipelines
AI that answers from your knowledge, with sources, not from guesswork.
How we deliver RAG Pipelines
Retrieval-Augmented Generation lets an AI system retrieve relevant information from company documents, internal knowledge bases, technical manuals, policies, reports and other approved sources before it generates a response.
We design and implement the full pipeline: ingestion and parsing, chunking strategies and metadata enrichment, embeddings and vector databases, semantic and hybrid retrieval, metadata filtering, reranking and context selection, then grounded generation with source attribution.
Retrieval is measured, not assumed. We evaluate retrieval quality, answer relevance and groundedness, and optimise the pipeline until it performs reliably on realistic questions.
What's included
RAG architecture & implementation
Document ingestion, parsing & preprocessing
Chunking strategies & metadata enrichment
Embedding generation & vector database integration
Semantic search & hybrid retrieval
Metadata filtering, reranking & context selection
Grounded answers with source attribution
Retrieval evaluation & pipeline optimisation
RAG Pipelines FAQs
Common questions about RAG Pipelines at Watsbit.
1What data can a RAG system use?
Any approved source: PDFs, Word documents, wikis, manuals, policies, reports, tickets and database records. Access controls are respected so users only retrieve what they are allowed to see.
2How is RAG different from fine-tuning?
RAG gives a model up-to-date knowledge at query time and can cite its sources. Fine-tuning changes how a model behaves, such as its terminology, tone or output format. Many systems use both.
Start your RAG Pipelines project
Book a free 30-minute consultation. We will map your idea to the right services, a realistic budget and a timeline.
