Fine-Tuning & Model Customization
Fine-tuning is simultaneously oversold and underused: oversold to teams whose real problem is retrieval or prompting, underused by teams paying frontier prices for a narrow task a tuned small model would do faster, cheaper, and inside their own perimeter. The skill isn't running the training job — it's knowing which situation you're in.
We start with evals and honesty: if better context engineering closes the gap, that's the recommendation, and it's cheaper. When tuning is justified — domain formats, tone at scale, latency-critical narrow tasks, distilling an expensive pipeline into a small model you own — we engineer it properly: dataset curation with quality gates, LoRA and parameter-efficient methods, eval-gated comparison against the untuned baseline, and a repeatable pipeline so the second tune costs a fraction of the first.
Honest feasibility assessment
Evals that show whether tuning beats prompting and retrieval — before you spend on training.
Dataset engineering
Training data curated, cleaned, and quality-gated — the part that actually determines outcomes.
LoRA and PEFT training
Parameter-efficient tuning with rigorous baseline comparison.
Distillation
Expensive model pipelines compressed into small models you own and serve cheaply.
Tuning pipelines
Repeatable train-eval-deploy loops, so customization is a capability rather than a one-off project.
Do we need fine-tuning?
Statistically, probably not yet: most teams asking for it have a retrieval or prompting gap that's cheaper to fix and easier to maintain. But for stable, narrow, high-volume tasks — or knowledge you need baked into a model you own — tuning wins decisively. A week of evals answers it with data.
How much training data do we need?
Less than you fear, cleaner than you have. Hundreds to a few thousand high-quality examples move behavior with LoRA; a million noisy ones mostly teach the model your noise. Dataset curation is where tuning projects are won, which is why it's most of our effort.
Won't fine-tuning lock us into a model that goes stale?
Only if the pipeline is a one-off. We build the train-eval-deploy loop as repeatable infrastructure, so re-tuning on a newer base model is a scheduled job with an eval gate — not a research project. The dataset is the durable asset; models are replaceable.
Put Fine-Tuning & Model Customization to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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