An LLM that can't see your data is a demo, not a product. LlamaIndex is built for exactly this gap — indexing, chunking, and retrieving your documents, databases, and internal systems so the model answers with your facts instead of guessing. We build LlamaIndex pipelines that stay accurate as your data grows, not proof-of-concepts that fall apart past a few hundred documents.
RAG is easy to demo and hard to get right in production. A dedicated team catches the retrieval and indexing mistakes that only surface once real data and real traffic show up.
Chunking strategy, metadata filtering, and re-ranking tuned to your content — not the default settings that work fine in a demo and poorly on your actual documents.
Internal documents, contracts, and proprietary data stay inside your infrastructure or a controlled cloud boundary — no data leaves the systems you approve.
Incremental updates, background re-indexing, and storage choices planned for the document volume you'll have in a year, not just the pilot dataset.
LlamaIndex when the job is data indexing and retrieval; LangChain or a custom pipeline when the job is orchestration and agents. We scope before we build.
Scoped to the data and query patterns your product actually needs — not a fixed RAG template.
End-to-end retrieval-augmented generation — ingestion, chunking, embedding, retrieval, and response synthesis.
Indexing PDFs, wikis, tickets, CRM records, and internal APIs into a queryable knowledge base.
Wiring LlamaIndex to Pinecone, Weaviate, pgvector, or your existing infrastructure based on scale and cost.
Retrieval accuracy testing and iteration so answers stay grounded as your document set grows and changes.
Depends on the job. LlamaIndex is purpose-built for indexing and retrieving your data — it's usually our first choice for RAG. LangChain is stronger for orchestrating multi-step agent workflows and tool chains. Many production systems use both; we scope which fits your case before writing code.
Yes. We index your documents inside infrastructure you control — your cloud account or on-prem — so nothing is sent to third parties beyond the LLM calls you've already approved. Access control and data boundaries are part of the initial scoping, not an afterthought.
Incremental indexing so new or updated documents don't require a full re-index, metadata filtering to keep retrieval fast as the corpus grows, and a vector store chosen for your actual query volume and budget rather than the default in a tutorial.
A focused pilot on a defined dataset typically takes a few weeks. Production hardening — evaluation, incremental indexing, access control, and monitoring — extends that depending on data volume and integration count. We give a fixed timeline after a scoping call.
Tell us what data you need an LLM to reason over — we'll scope a RAG approach and a fixed estimate.