About RAG Pipelines

RAG turns generic LLMs into experts on your data — docs, tickets, code, products, policies. We build production RAG with proper chunking strategies, hybrid search (vector + keyword), reranking, citation enforcement, and evals for retrieval quality.

Document ingestion pipelines

Smart chunking (semantic + structural)

Pinecone / Weaviate / pgvector

Hybrid search + reranking

Citation enforcement

Retrieval quality evals

Why Choose Our RAG Pipelines

Ground Truth from Your Data

LLMs answer from your docs, not training data guesses.

Source Citations

Every answer cites the source chunk so users can verify.

Scales to Millions

Handles millions of documents with sub-second retrieval.

Measurable Quality

Retrieval evals with recall@k and precision metrics.

Our RAG Pipelines Services

Document RAG

RAG over unstructured docs — PDFs, Word, HTML, markdown — with format-aware chunking and metadata filtering.

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Code RAG

RAG over codebases — for code assistants, docs generators, bug-lookup tools, and technical Q&A.

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Structured Data RAG

RAG over structured data — CSVs, databases, spreadsheets — with text-to-SQL and hybrid retrieval.

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RAG Pipelines

About RAG Pipelines

Why Choose Our RAG Pipelines

Ground Truth from Your Data

Source Citations

Scales to Millions

Measurable Quality

Our RAG Pipelines Services

Document RAG

Code RAG

Structured Data RAG

Other AI Solutions Services

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