01
Hierarchical document parsing
Chunking complex PDFs, tables and multi-page manuals with layout-aware parsers.
Technologies — Cloud, DevOps & AI
LlamaIndex enterprise RAG & data ingestion pipelines
We engineer production-grade RAG pipelines with LlamaIndex that ground answers in your documents, cite their sources, and are evaluated for faithfulness before release.
Core capabilities
Why we build with LlamaIndex
02
Query routing & sub-questions
Decomposing complex user questions into sub-queries routed to specialized vector indexes.
03
Reranking
Applying Cohere or ColBERT rerankers so the most relevant context sits at the top of the prompt.
Use cases
Where LlamaIndex fits
Financial & legal document Q&A
Questions answered over long SEC filings and contracts, with citations down to the table or clause.
Technical support assistants
Resolving customer tickets by retrieving relevant technical manual steps.
How we staff it
LlamaIndex engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
LlamaIndex uses semantic node parsing, sentence window retrieval, and cross-encoder rerankers to preserve context and reduce retrieval noise.
Yes. LlamaHub provides connectors for sources like Notion, Google Drive and SQL databases, and we schedule ingestion so the index stays in sync with the source.
Ecosystem
Related technologies
Planning a LlamaIndex project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.