We design, build and operate retrieval-augmented generation systems that turn your documents and databases into accurate, cited answers. Retrieval engineering first, evaluation from day one.
An LLM can only be as good as the context it is given. We bring deep search engineering to every RAG build, so the right passages reach the model every time.
Retrieval tuned on your data so the model sees the right passages - not just similar-looking ones.
Every answer links back to its sources, so users can verify it and you can debug it.
Document-level access control, data privacy and audit trails carried through to retrieval.
Latency and token spend measured and tuned, from thousands to billions of documents.
Answer employee questions from wikis, policies and tickets - with citations.
Natural-language questions over large contract, research or compliance repositories.
Draft grounded replies from product docs and past resolutions for your support team.
Chat-style search experiences that understand context and follow-up questions.
Find content by meaning, not just keywords, across every source you own.
Help engineers find the right function, runbook or design doc in seconds.
Pick the highest-value use case, audit your sources and build the evaluation set that defines success.
A working RAG pipeline on your real data, benchmarked against the eval set.
Access control, guardrails, latency and cost tuning, CI-driven evals and integration.
Monitoring and continuous relevance tuning - or a clean handover to your team.
RAG combines a retrieval system with a large language model. At query time it fetches the most relevant passages from your knowledge base - a search engine or vector database - and gives them to the LLM as context, so answers are grounded in your own, up-to-date data and can cite their sources.
We build on Elasticsearch, OpenSearch, Qdrant, Vespa, turbopuffer, Milvus, Pinecone and Weaviate. We help you choose based on scale, latency and cost targets, filtering needs and the infrastructure you already run - often your existing search cluster is the right place to start.
A working prototype on your real data, measured against an agreed evaluation set, typically takes 2-4 weeks. Hardening it for production - guardrails, security review, latency and cost tuning, monitoring - typically takes a further 4-8 weeks, depending on data volume and integrations.
Yes. Both Elasticsearch and OpenSearch support vector search natively, so we can add embeddings, hybrid (keyword + semantic) retrieval and reranking to the cluster you already operate, without introducing a separate vector database unless it is genuinely needed.
Before building, we agree on an evaluation set of real questions with expected answers. We then measure retrieval quality and answer faithfulness against it on every change, so improvements and regressions are visible rather than anecdotal.
Knowledge assistants over internal documentation, customer support copilots, document question-answering with citations, research assistants, semantic search over large repositories and code search. RAG fits whenever an LLM needs to answer from your specific data.
Tell us about your data and the questions your users ask. In a free 30-minute call we will tell you honestly whether RAG fits, what it would take, and how we would measure success.
