RAG Systems & Retrieval Pipelines
We build retrieval-augmented generation systems that ground LLMs in your own knowledge — accurate, cited, and fast enough for production.
RAG is the most reliable way to make an LLM answer from your data without retraining it. But a naive "embed and search" pipeline plateaus quickly: retrieval misses, irrelevant chunks, and confident-but-wrong answers.
We build RAG systems the way they need to be built for production — thoughtful chunking, the right embedding model, hybrid (dense + keyword) retrieval, reranking, and an evaluation loop that measures both retrieval quality and answer quality so you can improve with confidence.
What's included
- Document ingestion, parsing, and chunking strategy design
- Embedding model selection and vector index setup
- Hybrid search (dense + BM25) and reranking for precision
- Citation and source-grounding so every answer is traceable
- Retrieval and answer-quality evaluation harnesses
- Latency, caching, and cost optimisation for scale
How we approach it
- Build a labelled retrieval eval set from real user questions
- Tune chunking, embeddings, and retrieval against measurable recall
- Add reranking and grounding, then measure answer faithfulness
- Deploy with monitoring and a feedback loop for continuous improvement
What you get
- A production RAG service integrated with your product and data sources
- Retrieval + answer-quality evaluation dashboards
- A tuned, documented pipeline your team can extend
- Cost and latency benchmarks with optimisation recommendations
Technologies we use
Frequently asked questions
RAG or fine-tuning — which do we need?
Start with RAG. It grounds a model in your current data, is cheaper to iterate on, and updates instantly when your documents change. Fine-tuning complements RAG when you also need a specific behaviour or format. Our guide on building a RAG pipeline covers the trade-offs.
Why is our current RAG giving wrong answers?
Usually retrieval, not the model. If the right chunk never reaches the LLM, no prompt will fix it. We audit chunking, embeddings, and reranking, and add an evaluation set so improvements are measurable rather than guesswork.
Which vector database should we use?
It depends on scale, hosting preferences, and filtering needs. We help you choose — see our comparison of Pinecone, Weaviate, and Qdrant — or work with what you already run.
Ready to talk rag systems?
Tell us about your project and we'll respond within 24 hours with a clear next step.