RAG & Vector Search

Ground your models in your own data with retrieval-augmented generation (RAG) and vector search.

RAG that actually works in production

RAG connects an LLM to your knowledge base so answers are grounded in real documents instead of training data. We build the full pipeline:

  • Chunking & embedding: Document parsing, chunking strategies, and embedding models tuned to your content and language.
  • Vector search with Qdrant: Fast, filtered similarity search over millions of vectors with metadata-aware retrieval.
  • Retrieval quality: Hybrid search (dense + sparse), re-ranking, and metadata filtering to raise answer quality.
  • Evaluation: Groundedness and relevance evaluation on labelled sets so you can measure and improve.
  • Serving: The retrieval layer and LLM serving wired together as one dependable system.

What makes RAG fail (and how we avoid it)

Most RAG proof-of-concepts break in production for predictable reasons:

  • Bad chunking: Chunks too large or split mid-thought produce retrieval that looks relevant but isn’t. We get chunking right before tuning anything else.
  • No evaluation: You can’t improve retrieval you don’t measure. We establish hit-rate and answer quality baselines on labelled evaluation sets.
  • No data-residency plan: For regulated Swedish and EU organizations, embedding and retrieval layers must stay in-jurisdiction — a strong argument for self-hosted Qdrant.

What you can build

  • Enterprise search over internal documentation, Confluence, SharePoint, and Git repositories.
  • Customer-support assistants grounded in your knowledge base and ticket history.
  • Document Q&A for compliance, legal, technical specs, and regulatory content.
  • Code assistants that retrieve from your private repositories and documentation.

Our delivery model

  1. Data audit & strategy — Corpus analysis, language/domain assessment, and embedding model selection.
  2. Pipeline build — Ingestion (parsers, chunking, metadata extraction), embedding, Qdrant indexing, retrieval API.
  3. Quality loop — Evaluation harness, re-ranking, hybrid search tuning, and A/B testing framework.
  4. Production hardening — Kubernetes deployment, multi-tenancy, RBAC, backup/restore, and SLO monitoring.

Need RAG assistance?

We design and build RAG pipelines end-to-end. Talk to an engineer.