RAG Pipeline
Development Company
RAG (Retrieval-Augmented Generation) development involves building systems that combine vector search with LLMs to answer questions using private data. Ubikon builds production-ready RAG pipelines with document ingestion, hybrid search, reranking, and evaluation -- so your AI gives accurate, cited answers from your own knowledge base.
RAG Pipeline Architecture
Ingest
Load documents from PDFs, Word, Confluence, Notion, databases, and APIs.
Chunk
Split documents into semantically meaningful chunks with overlap for context.
Embed
Convert chunks into vector embeddings using OpenAI, Cohere, or open-source models.
Store
Index vectors in Pinecone, Qdrant, Weaviate, or pgvector with metadata.
Retrieve
Hybrid search combining semantic vectors + keyword BM25 for best recall.
Rerank
Cross-encoder reranking to surface the most relevant chunks.
Generate
LLM generates grounded, cited answers using retrieved context.
RAG Components We Develop
Document Ingestion
Multi-format loaders for PDFs, DOCX, HTML, Markdown, CSV, and API sources. Scheduled re-ingestion for keeping data current.
Chunking Strategies
Recursive, semantic, and sentence-window chunking. Configurable chunk sizes and overlap. Parent-child document relationships.
Vector Databases
Pinecone, Qdrant, Weaviate, Chroma, and pgvector. Metadata filtering, namespace isolation, and multi-tenant support.
Hybrid Search
Combine dense vector search with sparse keyword search (BM25) for better recall. Reciprocal rank fusion for result merging.
Reranking
Cross-encoder models (Cohere Rerank, BGE Reranker) to re-score and filter retrieved chunks for higher precision.
Evaluation
Automated evaluation pipeline measuring faithfulness, answer relevancy, context precision, and context recall. A/B testing for production.
Where RAG Delivers Value
Legal Document Q&A
Lawyers query 50,000+ legal documents and get cited answers in seconds instead of hours of manual research.
Knowledge Base
Internal Q&A over SOPs, policies, and documentation. Employees get instant answers with source citations.
Customer Support
Support agents get real-time answer suggestions from product docs, past tickets, and knowledge base articles.
Medical Records
Clinicians query patient records, research papers, and treatment protocols with privacy-compliant RAG systems.
RAG vs Fine-Tuning
| Factor | RAG(Recommended) | Fine-Tuning |
|---|---|---|
| Best For | Factual Q&A over documents | Changing model behavior/style |
| Data Updates | Real-time (re-index docs) | Requires retraining |
| Cost | $15K-70K setup + $100-1K/mo | $20K-100K + GPU costs |
| Accuracy | High (grounded in source docs) | Medium (can still hallucinate) |
| Citations | Yes (source documents) | No |
| Setup Time | 4-10 weeks | 6-16 weeks |
| Data Privacy | Data stays in your infra | Data sent to training pipeline |
| Maintenance | Re-index when docs change | Retrain periodically |
Frequently Asked Questions
What is RAG and how does it work?
RAG (Retrieval-Augmented Generation) is a technique that combines vector search with LLMs to answer questions using private data. Documents are chunked, converted to vector embeddings, and stored in a vector database. At query time, relevant chunks are retrieved via semantic search and sent to the LLM as context to generate accurate, grounded answers with citations.
How much does RAG development cost?
A basic RAG system costs $15,000-$30,000. Production RAG with hybrid search, reranking, and evaluation costs $30,000-$70,000. Enterprise RAG with multi-source ingestion, compliance, and multi-tenant support costs $70,000-$150,000+.
RAG vs fine-tuning: which is better?
RAG is better for factual Q&A over documents, when data changes frequently, and when you need citations. Fine-tuning is better for changing model behavior, tone, or output format. Most production systems use RAG because it provides verifiable answers, costs less to maintain, and data can be updated without retraining.
What vector databases do you use?
We work with Pinecone (managed, scalable), Qdrant (open-source, fast), Weaviate (hybrid search built-in), Chroma (lightweight, local), and pgvector (PostgreSQL extension). We recommend based on your scale, latency, and infrastructure preferences.
How do you evaluate RAG system quality?
We use automated evaluation with metrics like faithfulness (is the answer grounded in context?), answer relevancy (does it address the question?), context precision (are retrieved chunks relevant?), and context recall (are all relevant chunks found?). We also set up human evaluation pipelines and A/B testing.
Ready to Build a RAG System?
Get a free RAG consultation. We will analyze your data sources, recommend the right architecture, and deliver a proof of concept in 2-3 weeks.