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LlamaIndex RAG & Data Framework Development
A data framework for connecting LLMs to your documents, where ingestion and retrieval quality decide the result.
Start Your ProjectWhat LlamaIndex is
LlamaIndex is an open-source framework focused on connecting language models to your own data. It provides loaders for many document and data sources, ways to parse and split content, index structures, retrieval strategies and query engines, along with agent and workflow tooling built on top.
Compared with a general orchestration framework, its centre of gravity is the data side of retrieval-augmented generation: getting messy PDFs, tables and records into a form a model can use, and retrieving the right pieces. That is where most RAG systems succeed or fail, so a data-first framework can be a good fit.
Suitability
And when it is not. Choosing the wrong tool costs more than any implementation detail.
Good fit for
- Question answering over large or varied document collections, including PDFs with tables and structured records
- Projects where ingestion, parsing and retrieval tuning are the hard part
- Combining several data sources behind one query interface with source citations
Consider alternatives for
- A small, clean knowledge base where a few lines of direct retrieval code and one vector query are enough
- Agent-heavy applications where the main complexity is tool orchestration rather than retrieval, which a general framework may suit better
- Teams unwilling to invest in an evaluation set, since no framework can rescue an unmeasured retrieval pipeline
In practice
01
Document question answering
Ingestion and indexing pipelines over a client's PDFs, wikis and records, with answers that cite their source passages so users can check them.
02
Retrieval evaluation and tuning
Measuring retrieval separately from generation — whether the right passage was found — and tuning chunking, metadata filters and re-ranking against a labelled question set.
03
Multi-source query layers
A single interface over documents and structured data, with access control applied at retrieval time so users only see what they are permitted to see.
Considerations
Practical notes from running this in production.
- 01
We take LlamaIndex on per engagement, using it alongside or instead of other frameworks depending on fit. Often a thin custom retrieval layer is the better choice, and we recommend it when so.
- 02
Retrieval quality depends on parsing. Scanned pages, multi-column layouts and tables need dedicated handling, and poor parsing quietly caps accuracy regardless of the model.
- 03
Permissions must be enforced at retrieval, not left to the prompt. Index-level metadata and per-user filtering are design requirements for any multi-user knowledge base.
- 04
Framework APIs evolve quickly. Pin versions and isolate framework calls behind your own interface so an upgrade does not ripple through the application.
FAQ
LlamaIndex or LangChain?
They overlap. LlamaIndex leans toward the data and retrieval side; LangChain toward general orchestration and agents. Plenty of systems use one, the other or neither. We choose by what the project's hard problem is, and for simple cases we skip frameworks entirely.
How do we know the answers are accurate?
We build a labelled question set from real user questions, then measure retrieval and answer quality separately and track both over time. Citations to source passages let reviewers verify individual answers, and low-confidence cases can be routed to a person.
How long does a LlamaIndex development project take?
It depends on scope. A focused first release is usually weeks rather than months, while larger platforms are delivered in phases. We give a written estimate after a short discovery conversation and agree milestones before work starts.
How is LlamaIndex development work priced?
We work either to a fixed scope with agreed milestones or on a time-and-materials basis, whichever suits how well the requirements are known. You receive the estimate and its assumptions in writing before committing.
Who owns the code and the intellectual property?
Ownership terms are agreed in writing before work begins, so there is no ambiguity about what you receive and what you may reuse.
By industry
Where we apply LlamaIndex. Tiles marked "Guide" open a dedicated write-up; the rest open the industry page.
Let’s talk about your LlamaIndex project
Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.