We build the evaluation harness before touching a model, so better is a number rather than an impression. Then dataset construction, usually the part that decides the outcome, followed by a full fine-tune, LoRA or QLoRA, or preference tuning, whichever the task and the budget justify.
A general-purpose model does not know your domain, your terminology or your documents, and no amount of prompt engineering is fixing it.
We build the evaluation harness before touching a model, so better is a number rather than an impression. Then dataset construction, usually the part that decides the outcome, followed by a full fine-tune, LoRA or QLoRA, or preference tuning, whichever the task and the budget justify.
A model measurably better than the baseline on the cases you care about, an evaluation harness you keep and can re-run, and a deployment you own, hosted, self-hosted or on-device.
Training, fine-tuning, retrieval, agents, and knowing when not to.
A working prototype in 1 to 3 days. Training, evaluation and tuning run weeks to months, and a genuine research problem can run to eight. It depends entirely on your data and the target.
Security review, automated usability testing, vulnerability scanning in CI, and the edge and bot policy that keeps a product reachable but not scrapeable.
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