LlamaIndex.

Where LlamaIndex fits on a long engagement.

Its narrower focus is an advantage over general frameworks for retrieval work. Ingestion connectors, chunking strategies, several index types and query engines that decompose complex questions are all built around the specific problem of getting the right context to a model.

The index strategies are where the thinking is. A vector index for semantic similarity, a summary index for questions spanning a whole document, a knowledge graph index for relationship queries — these answer genuinely different question shapes, and using one strategy for all of them is why many retrieval systems handle some questions well and others poorly.

What an assigned team does with LlamaIndex.

Document ingestion is unglamorous and decisive. PDFs with multi-column layouts, tables that lose structure when flattened, and scanned documents needing OCR all degrade retrieval quality before any model is involved.

Getting extraction right is often the highest-return work in a retrieval system, and it is scoped explicitly rather than assumed under managed ai services.

What we use LlamaIndex for.

• Index strategy matched to question type Summary and graph indexes where similarity search answers the wrong shape of question.
• Extraction that preserves structure Tables and layout handled, because flattening them degrades retrieval before the model runs.
• Complex questions decomposed Query engines that break a multi-part question into retrievable sub-questions.

How LlamaIndex capacity is assigned.

Retrieval application work is assigned inside AI capacity, with document extraction quality treated as a first-class deliverable.