LlamaIndex development for AI that answers from your data
LlamaIndex is an open-source framework focused on connecting language models to private data. It handles loading documents, splitting and indexing them, and retrieving the most relevant passages so a model can answer questions grounded in your own content.
Retrieval quality depends on details such as how documents are parsed, how text is chunked and how results are ranked, and LlamaIndex provides well-tested components for each step, plus connectors for many file types and data sources. It is especially strong for knowledge bases and document question answering. For workflows centred on tools and actions rather than documents, LangChain or direct API calls may be simpler. Both frameworks can be used together.
What we build with LlamaIndex
- Knowledge-base and policy assistants
- Question answering over PDFs and manuals
- Search across multiple internal data sources
- Document ingestion pipelines
- Citation-backed answers for staff
LlamaIndex questions
What is retrieval-augmented generation?
It means finding the most relevant passages from your own data and giving them to the model with the question, so the answer is based on your content rather than general knowledge.
Can answers include sources?
Yes. We design retrieval so each answer can cite the documents or passages it used, which helps users verify it.
What kinds of documents can LlamaIndex work with?
It has loaders for PDFs, Word files, web pages, databases and many business tools. Complex PDFs with tables or scans often need specialist parsing, which we test on your real files.
How do you keep answers limited to our content?
We instruct the model to answer only from retrieved passages, return a clear "not found" when nothing relevant appears and test with questions the documents cannot answer.
How do you measure retrieval quality?
We build a question set with known correct sources and measure whether the right passages are retrieved and whether answers are faithful to them, then tune chunking and search settings.
Which vector database works with LlamaIndex?
LlamaIndex supports many stores, including PostgreSQL with pgvector and dedicated vector databases. We usually prefer one that fits your existing infrastructure.
How are document updates reflected in answers?
We set up ingestion so new, changed and deleted documents are re-indexed automatically, keeping answers in line with the current version of your content.
Can users only see answers from documents they are allowed to access?
Yes. We store permission metadata with each passage and filter retrieval by the user's access rights, so restricted content never reaches the model for that user.
Can LlamaIndex use a self-hosted model?
Yes. It works with hosted APIs and with open models and embedding models running on your own infrastructure, which helps when data must stay in-house.
Is retrieval better than fine-tuning a model on our documents?
For answering questions from changing documents, retrieval is usually more accurate, cheaper to update and able to cite sources. Fine-tuning suits style or task behaviour rather than facts.
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