Service
LLM integration & advisory
We help teams adopt large language models with engineering discipline: what to build, which model to use, when fine-tuning is worth it, and how to prove the system works before it meets customers.
What this covers
- Feasibility & architecture — a short, honest assessment of whether your use case needs an LLM at all, and the simplest architecture that meets it.
- Model selection & cost — hosted frontier models vs. open-weight, latency and cost modeling at your real traffic, and exit strategies so you are not locked in.
- Prompting, RAG, or fine-tuning — chosen from evidence on your data, in that order of preference, because each step up costs more to build and maintain.
- Evaluation & safety — golden sets, regression harnesses, red-team passes, and guardrails appropriate to your risk profile.
Where it works well
Teams about to commit significant budget to an AI feature, organizations burned by a demo that never reached production, and companies that need a second opinion in plain language — English or Korean — before signing a vendor contract.
Common questions
- Do you resell a particular model or platform?
- No. We have no reseller agreements, so recommendations follow your requirements and measured results rather than a commission.
- Can you work with our in-house developers?
- That is the preferred mode. We design alongside your team and hand over code, evals, and runbooks so capability stays in-house after the engagement.
- What does an advisory engagement cost?
- Advisory work is scoped as a short fixed-price assessment first, so you know the full cost before committing. Contact us with your use case for a same-week estimate.