The data layer
under your RAG system.
LlamaIndex handles the indexing and retrieval plumbing of a RAG pipeline — connectors into your actual data sources, index structures beyond flat similarity search, and evaluation tooling to check whether retrieval is actually working.
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Indexing,
not just embedding.
LlamaIndex is a data framework built specifically for retrieval-augmented generation — it provides connectors into real data sources (documents, databases, APIs), index structures beyond flat vector similarity, and evaluation utilities for checking retrieval quality before launch.
We reach for it when a RAG pipeline needs more structure than a vector database alone provides — complex document hierarchies, multiple data sources, or retrieval strategies beyond simple nearest-neighbor search.
Structure where
flat search falls short.
The same retrieval discipline we apply to every RAG build, with LlamaIndex's extra structure where it earns its keep.
Ingesting from the data sources you actually have, not just clean text files.
Choosing retrieval strategies — hierarchical, keyword-hybrid, or flat — based on what the data and queries actually need.
Golden-set testing before launch, the same standard we hold every RAG system to.
Where this fits
LlamaIndex is one option in the data/indexing layer of our RAG development work.
Before you
book a call.
The questions we get asked most about LlamaIndex and RAG data pipelines — answered straight, no sales pitch.
How is LlamaIndex different from just using a vector database directly?
A vector database stores and searches embeddings. LlamaIndex sits a layer above that, handling data ingestion, index structure, and retrieval strategy — useful when a project has multiple data sources or needs more than flat similarity search.
Does using LlamaIndex lock us into a specific vector database?
No — it works with multiple vector store backends, including ChromaDB, which is our default when self-hosting matters.
Do you always use LlamaIndex for RAG builds?
No — for simpler pipelines, a vector database plus a thin custom retrieval layer is often enough. We reach for LlamaIndex when the data and query patterns genuinely need its structure.
Tell us what
you're trying to retrieve.
Book a 30-minute call — we'll tell you honestly whether your RAG system needs this level of structure, or whether something simpler works.