Hire LlamaIndex Engineering
for RAG & knowledge retrieval

From enterprise document search and multi-source RAG pipelines to knowledge graphs and hybrid retrieval systems, our AI engineers build LlamaIndex solutions that make your data accessible to AI at production scale.

Discuss your RAG project

20+

RAG systems built

160+

data connectors supported

30+

AI engineers specializing in RAG

Core Capabilities

What we build
with LlamaIndex

RAG Pipelines

Multi-source document retrieval

Production RAG systems that ingest documents from 160+ sources, chunk intelligently, embed with state-of-the-art models, store in vector databases, and retrieve with hybrid search — giving LLMs accurate, grounded answers from your private data.

Knowledge Graphs

Structured entity & relationship indexing

Knowledge graph indexes that capture entity relationships, semantic connections, and structured facts across your documents — enabling multi-hop reasoning and complex queries that flat vector search cannot answer.

Enterprise Document Search

Secure, access-controlled AI search

Enterprise knowledge bases with role-based access filtering, multi-tenancy for organizational isolation, incremental indexing for real-time freshness, and chat interfaces that cite exact source documents for every AI-generated answer.

How It Works

From raw data to
intelligent retrieval

Data Ingestion &
Index Design

We connect to your data sources, configure document parsers, choose chunking strategies that preserve semantic coherence, and design the right index type — vector, keyword, or knowledge graph — for your query patterns.

Retrieval
Engineering

Our AI engineers implement and tune retrieval pipelines — hybrid search, reranking, query expansion, and recursive retrieval for complex multi-hop questions — until accuracy meets your benchmark.

Evaluation &
RAG Quality

We evaluate RAG quality using faithfulness, context relevancy, and answer correctness metrics from Ragas and custom benchmarks. Our QA team validates every retrieval pipeline before production.

Deployment &
Index Maintenance

We deploy RAG systems as FastAPI services on Kubernetes, configure incremental index updates, monitor retrieval latency and answer quality, and set up alerting for index staleness and retrieval degradation.

Hire RAG Engineers

LlamaIndex engineers ready
to join your team

Grow your AI team with dedicated LlamaIndex engineers who build accurate, production-ready RAG systems and enterprise knowledge retrieval solutions.

Hire RAG Engineers

Multi-source RAG pipeline design & retrieval engineering

Hybrid search with BM25, reranking & HyDE

Knowledge graph indexes & structured entity retrieval

Pinecone, Weaviate & pgvector database integration

RAG evaluation with Ragas & access-controlled enterprise search

AI + LlamaIndex

RAG that actually
gets it right

Advanced
retrieval techniques

Beyond basic vector search — we implement query decomposition for complex questions, recursive retrieval for multi-hop reasoning, sentence window retrieval for better context, and HyDE for improved semantic matching.

Continuous RAG
evaluation

Automated RAG quality pipelines using Ragas — measuring faithfulness, answer relevancy, and context precision on every build, so retrieval quality never silently degrades as your data grows.

Incremental
indexing

New documents, updated pages, and modified records are indexed automatically without full re-indexing — keeping your RAG system's knowledge current in real time as your data sources evolve.

Grounded answers
with citations

Every AI answer is grounded in retrieved source chunks with explicit citations — document name, page number, and exact passage — giving users confidence and giving compliance teams a full audit trail of where each answer came from.

FAQ

Frequently Asked
Questions

LlamaIndex is a data framework for connecting LLMs to your private data — documents, databases, APIs, and knowledge bases. Use it when you need to build RAG (retrieval-augmented generation) systems that let AI answer questions from your own data rather than relying solely on what the model was trained on.

LlamaIndex is purpose-built for data indexing and retrieval — it has more sophisticated document parsing, chunking strategies, index types (vector, keyword, knowledge graph), and retrieval techniques out of the box. LangChain is better for orchestrating chains and agents. For complex RAG systems, we often combine both: LlamaIndex for retrieval and LangChain/LangGraph for agent orchestration.

LlamaIndex supports 160+ data connectors — PDFs, Word documents, Notion, Confluence, Google Drive, databases (PostgreSQL, MySQL, MongoDB), APIs, web pages, email, Slack, and more. We use LlamaHub integrations and build custom connectors for proprietary enterprise systems.

Basic vector search retrieves semantically similar chunks but misses structured information and exact matches. We improve accuracy with hybrid search (vector + BM25), reranking models, HyDE (hypothetical document embeddings), query decomposition, recursive retrieval for multi-hop questions, and knowledge graphs for structured entity relationships.

Yes. We build enterprise RAG systems that index millions of documents across multiple data sources — with incremental indexing to keep knowledge fresh, access control filtering so users only see authorized content, multi-tenancy for isolated organizational knowledge stores, and sub-second retrieval with optimized vector databases like Pinecone, Weaviate, or pgvector.