Service
AI agents & RAG systems
We design and build AI agents and retrieval-augmented generation (RAG) systems that answer from your data — documents, tickets, logs, manuals — with citations, guardrails, and measurable accuracy.
What this covers
- RAG over private knowledge — chunking and indexing strategy, hybrid retrieval, reranking, citation-grounded answers over manuals, wikis, and archives in English and Korean.
- Multi-agent workflows — planner–worker architectures that decompose real business tasks: triage, investigation, report drafting, tool use against your internal APIs.
- Evaluation you can trust — a golden-set harness with retrieval and answer metrics, so “it seems better” becomes a number your team can rerun after every change.
- Deployment — your cloud or on-premise, with logging, cost controls, and fallbacks when the model is wrong or unavailable.
Where it works well
Support and operations teams answering from large document sets, engineering organizations mining logs and postmortems, and any workflow where staff spend hours finding what the company already knows. Bilingual Korean–English corpora are a specialty.
Common questions
- How is this different from just using ChatGPT?
- A general chatbot does not know your documents, cannot cite them, and cannot be measured against your ground truth. A RAG system retrieves from your own indexed knowledge, answers with citations, and ships with an evaluation harness that proves accuracy on your data.
- Can it run fully on-premise?
- Yes. We deploy with open-weight models on your hardware when data cannot leave your network, and we are honest about the quality trade-offs versus hosted frontier models.
- How long until a working prototype?
- Typically two to six weeks after scoping, depending on data readiness. The prototype runs on your real documents, not a demo corpus.