Retrieval-Augmented Generation (RAG)
Grounded AI answers, backed by your knowledge
LLMs make things up. Retrieval-Augmented Generation (RAG) fixes that by retrieving your real documents before answering. Our RAG development services cover the full build — chunking, embedding, reranking, and evaluation — so answers stay grounded and every citation is real.
01Overview
What our RAG development services deliver
RAG sounds simple and rarely is. Chunking strategy, embedding choice, reranker tuning, and prompt design all affect answer quality. Whether you need an enterprise RAG knowledge base, a document-search platform, or a domain-specific assistant, we design the retrieval layer around your actual corpus — not a generic template. RAG also pairs well with AI agents that act on what they retrieve. We've built RAG systems across coaching, research, and document intelligence, and we know where the edge cases bite.
02Why choose this service
Why choose our RAG systems
01
Grounded answers
Responses backed by your source documents, with inline citations for every claim.
02
Tuned retrieval
Chunking, embedding, and reranking configured for your specific document types.
03
Measurable quality
Evaluation pipelines that catch retrieval regressions before they hit users.
04
Vendor-agnostic
OpenAI, Anthropic, Gemini, or open-source. Qdrant, Pinecone, pgvector. We swap without rewrites.
03How we work
How we build your RAG system
01
Corpus Design
Source documents, access control, refresh strategy, and chunking approach.
02
Indexing Pipeline
Embeddings, vector store setup, and metadata filtering.
03
Retrieval & Generation
Query expansion, reranking, prompt design, and streaming generation with citations.
04
Evaluation & Monitoring
Golden test sets, answer quality evaluation, and drift monitoring in production.
04Applications
RAG use cases we build
05Technologies
RAG tech stack we use
06FAQ
Frequently asked questions about RAG
What is Retrieval-Augmented Generation (RAG)?
RAG is a technique that retrieves relevant information from your own documents and data before the language model generates an answer. Instead of relying on what the model memorised during training, it answers from your real, current sources — with citations you can verify.
How does RAG stop AI hallucinations?
By grounding every response in retrieved source material. The model is instructed to answer only from the documents it retrieved, and each claim carries an inline citation — so unsupported answers are visible immediately rather than presented as fact.
RAG vs fine-tuning: which do we need?
RAG is the right choice when your knowledge changes often or must be cited — it updates the moment your documents update. Fine-tuning suits fixed tone, format, or specialised behaviour. Many production systems use both, and we can scope which applies to your case.
How long does it take to build a RAG system?
A focused proof of concept typically takes weeks, not months. Full production deployment depends on corpus size, access-control requirements, and integration — we scope this before we start.
How much data do we need for RAG?
As few as a dozen documents works for narrow domains. Larger corpora need more tuning on chunking and reranking.
How do you evaluate RAG quality?
Golden test sets with expected answers, retrieval hit rate, citation accuracy, and blind human reviews.
07Explore more
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