RAG Development Services
RAG Development Services — Ground Your AI in Real Data
We build retrieval-augmented generation pipelines that connect your LLMs to your actual company knowledge — delivering accurate, source-cited answers instead of hallucinations.
What We Build
Vector Database Setup
We architect and deploy vector databases (Pinecone, pgvector, Weaviate, Chroma) optimized for your data volume, query patterns, and latency requirements.
Knowledge Base Ingestion
Ingest documents, PDFs, wikis, databases, and APIs into your knowledge base — with chunking strategies, metadata tagging, and refresh pipelines.
Hybrid Search & Reranking
Combine semantic and keyword search with reranking models to maximize retrieval accuracy — ensuring the LLM always gets the most relevant context.
Advanced RAG Architectures
Implement advanced patterns: parent-child chunking, HyDE, multi-query retrieval, self-query, and agentic RAG for complex, multi-step question answering.
What You Get
A fully operational RAG system — from ingestion to retrieval to LLM response — complete with evaluation metrics and an ongoing refresh pipeline.
- RAG architecture design and technology selection
- Document ingestion pipeline with chunking and metadata
- Vector database setup, indexing, and optimization
- Hybrid search with semantic and keyword retrieval
- LLM response generation with source citation
- Evaluation framework for retrieval and generation quality
Tech Stack
Related Services
Ready to build something that lasts?
From initial scoping to production deployment — we partner with you end-to-end. Let's start with a conversation.