Most tasks don't need your biggest, most expensive model — they need one trained on your data for exactly what you're asking it to do. We fine-tune and distil models that are faster, cheaper, and often more accurate than the general-purpose model they replace.
Data preparation, evaluation, and versioning — the full system that makes a custom model maintainable.
Clean, de-duplicated, and structured examples with clear evaluation splits — versioned like code.
Domain adaptation of open or proprietary models, or distillation into a smaller, faster model.
Every candidate model benchmarked against your current baseline before it ships.
Ongoing checks that the new model doesn't quietly get worse on cases it used to get right.
Shipped behind your existing interface, with a clean path back to the baseline if needed.
The training topology and the evaluation pattern — not a slide about “AI customisation.”
// elhaa Fine-Tune Evaluation Harness const comparison = await elhaaEval.compare({ baseline: 'gpt-4o', candidate: 'ft:gpt-4o-mini:elhaa:v3', evalSet: 'domain-tasks-v2', metrics: ['accuracy', 'latencyMs', 'costPer1k'] });
A general-purpose model classified and routed support tickets accurately, but at a per-ticket cost that made the feature a net loss at the company's actual support volume.
We fine-tuned a small open-weight model on 4,000 historically-labelled tickets, distilled the classification behaviour from the original large model, and shipped it behind the same routing interface with a rollback path.
*Illustrative example based on a representative engagement.
Identify which task justifies a custom model, and whether you have enough labelled data.
Clean, de-duplicate, and structure training examples with clear evaluation splits.
Fine-tune or distil candidate models, benchmarked against the current general-purpose baseline.
Ship the winning model behind the same interface, with rollback to the baseline if needed.
Fine-tuned model measured head-to-head against the general-purpose model it replaces.
The core promise of distillation — tracked explicitly, not assumed.
Share of previously-correct cases the new model gets wrong — must trend to zero.
How often the model is refreshed as your data and task drift.
It varies by task, but useful results often start from a few hundred well-labelled examples; a few thousand gives more headroom. The task and data audit tells you exactly where you stand before we commit to training.
It can be — a fine-tuned open-weight model can run entirely inside your own infrastructure, so no prompts or data ever leave your boundary. We'll tell you honestly if your task doesn't justify that complexity.
Fine-tuning adapts a model to your specific task and data. Distillation trains a smaller, cheaper model to mimic a larger one's behaviour on that task. We often do both together: distil for cost, fine-tune for accuracy.
It's benchmarked head-to-head against your current baseline on a held-out evaluation set before it ever reaches production — no deploy without a measured win.
We set a retrain cadence appropriate to how fast your task drifts, and monitor for regression so you know when a refresh is actually needed rather than guessing.
A 30-minute call. We'll tell you honestly whether this is the right solution — and what it would take.
A short form, then a 30-minute call. We reply within one working day.
We'll be in touch within one working day.