Fine-tuning is the answer to a narrow question: the model knows the facts but will not hold the format, the tone, or a house structure across every output. It is the wrong answer to missing knowledge, which is retrieval. We train against an eval set and publish the comparison against the base model with prompting, because a fine-tune that cannot beat a good prompt has not earned its maintenance cost.
01 / FINE_TUNING
For behavior a prompt cannot hold. Trained against an eval set, with the base-model comparison that proves it earned its place.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
Fine-tuning is the answer to format and behavior, not to missing knowledge. Saying that out loud costs us work and saves clients a maintenance burden they did not need.
On Brandiligence, a fine-tuned model plus retrieval took five-part template adherence from 40% to 98%. Without the baseline comparison that number would mean nothing.
Base models improve and a fine-tune frozen against an old one silently falls behind. The retraining path ships with the model.
Week 1
Most fine-tuning requests are prompting problems or retrieval problems. We check first, and say so when that is the answer.
Week 1-3
Constructed and cleaned from your existing outputs, which is where the house format already lives. Dataset quality decides the result far more than the method.
Week 3-4
Against the prompted base model, on a held-out set, published either way.
Week 4-6
Requirements move and base models improve. A fine-tune with no retraining path is a liability with a shelf life.
Production traces are curated into a labelled, deduplicated, split dataset — the step that decides whether any of this works. A training run produces an adapter, and an eval harness scores it against the base model on held-out data. Only a run that wins reaches the versioned registry and serving. A regression goes back to the data, not to the trainer, which is the arrow most fine-tuning projects are missing.
Production-proven
Built by engineers who've already shipped this in production.
RAG when the failures are missing or stale facts; fine-tuning when they are format, tone or structure. They compose, and most production systems end up using both.
Often a few hundred good examples rather than thousands of mediocre ones. Your existing approved outputs are usually the dataset, already written.
You do, along with the dataset, which is the more valuable of the two. It outlives any particular base model.
You retrain, which is why the dataset and the pipeline are the deliverable rather than just the weights.
Eval suites and regression testing, for teams shipping on opinion instead of a number.
View serviceOpen models on your own infrastructure, for teams who will not call an API.
View serviceEvals, routing, and audit trails for teams whose demo cannot survive real traffic.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.