Vector & RAG Engineering
Enterprise RAG Development Services
Unlock your organization's internal knowledge. We build high-throughput Retrieval-Augmented Generation (RAG) pipelines with sub-100ms vector search, multi-modal ingestion, and verifiable citations.
Build RAG Architecture →Production RAG Architecture
- • Advanced Chunking & Metadata Filtering
- • Hybrid Vector + Full-Text BM25 Search
- • Reranking Pipelines (Cohere Rerank / BGE)
- • Automated Vector Index Synchronization
Zero Hallucination Guarantee
Our RAG pipelines enforce strict citation constraints so every AI response references the exact document page, sentence, or database ID used during generation.
LLM Development Services →Frequently Asked Questions
What is Retrieval-Augmented Generation (RAG) and why do enterprise companies need it?
RAG connects LLMs directly to your private enterprise data (PDFs, SQL databases, Notion, Slack, internal codebases) so the AI provides accurate, hallucination-free answers with exact citations.
Which vector databases do you use for enterprise RAG?
We utilize Pinecone, Qdrant, Weaviate, Milvus, and pgvector depending on compliance, latency, and hosting requirements.
Ready to Build Enterprise RAG?
Talk directly with our RAG engineers today.