Retrieval-Augmented Generation (RAG) System Engineering
Build secure Retrieval-Augmented Generation systems to connect LLMs with private company data.
Service Overview
Enterprise RAG Architecture & Data Flow
Retrieval-Augmented Generation (RAG) connects LLMs with private data without fine-tuning. We design RAG pipelines to convert PDFs, spreadsheets, and SQL tables into text chunks. We run embedding models to convert text into vector representations, storing them in high-speed vector databases like Pinecone, Milvus, or pgvector. When a query is received, the system retrieves relevant chunks to construct answers.
Document Parsing & Chunking Strategies
The performance of a RAG system depends on data quality. We build document parsing pipelines to extract text, tables, and images from complex documents. We implement chunking strategies (such as semantic chunking or sliding window chunking) to preserve context, ensuring retrieved data contains complete answers.
Semantic Search, Reranking & Retrieval Optimizations
Basic keyword search can miss semantic context. We build search pipelines combining keyword queries (BM25) with vector search (hybrid search). We integrate reranking models (like Cohere Rerank) to evaluate retrieved chunks, sending only the most relevant context to the LLM to improve response quality and reduce API costs.
Source Citations & Hallucination Prevention
AI hallucination is a primary concern for business applications. We configure prompts to instruct the LLM to base answers only on retrieved context. We build user interfaces displaying citations and links to source documents, allowing users to verify AI answers and building trust in the system.
Key Business Benefits
- Connects LLMs to your private documentation
- Reduces AI hallucinations with sourced answers
- Enables searching unstructured file libraries
- Keeps data secure inside private vector databases
Technical Capabilities
Technologies Used
Scope & Budget
Estimation framework based on custom feature modules.
Learn More & Case Studies
Industry Focus
Success Stories
Frequently Asked Questions
How does RAG compare to training a new model?
RAG is faster and cheaper than training a new model. It allows you to update your source files instantly without retraining, and ensures the AI cites its sources.
Ready to launch your Retrieval-Augmented Generation (RAG) System Engineering project?
Contact our product team to outline feature sets, select databases, and map timelines.