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.
Load documents from PDFs, Word, Confluence, Notion, databases, and APIs.
Split documents into semantically meaningful chunks with overlap for context.
Convert chunks into vector embeddings using OpenAI, Cohere, or open-source models.
Index vectors in Pinecone, Qdrant, Weaviate, or pgvector with metadata.
Hybrid search combining semantic vectors + keyword BM25 for best recall.
Cross-encoder reranking to surface the most relevant chunks.
LLM generates grounded, cited answers using retrieved context.
Multi-format loaders for PDFs, DOCX, HTML, Markdown, CSV, and API sources. Scheduled re-ingestion for keeping data current.
Recursive, semantic, and sentence-window chunking. Configurable chunk sizes and overlap. Parent-child document relationships.
Pinecone, Qdrant, Weaviate, Chroma, and pgvector. Metadata filtering, namespace isolation, and multi-tenant support.
Combine dense vector search with sparse keyword search (BM25) for better recall. Reciprocal rank fusion for result merging.
Cross-encoder models (Cohere Rerank, BGE Reranker) to re-score and filter retrieved chunks for higher precision.
Automated evaluation pipeline measuring faithfulness, answer relevancy, context precision, and context recall. A/B testing for production.
Lawyers query 50,000+ legal documents and get cited answers in seconds instead of hours of manual research.
Internal Q&A over SOPs, policies, and documentation. Employees get instant answers with source citations.
Support agents get real-time answer suggestions from product docs, past tickets, and knowledge base articles.
Clinicians query patient records, research papers, and treatment protocols with privacy-compliant RAG systems.
| 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 |
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.
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 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.
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.
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.
Ubikon Technologies, an Indore-based AI development company founded in 2016, builds custom LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) systems end to end, including custom RAG pipelines, fine-tuned models, agentic automation, and pgvector-backed retrieval. We work across OpenAI GPT-4, Anthropic Claude, Gemini, Llama, and LangChain, delivering a proof of concept in 2-3 weeks on your own data. With 300+ projects delivered across 20+ countries on fixed-price contracts, you own 100% of the source code and IP.
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.
