Train stage
Best for
Teams whose prompts have hit a ceiling on accuracy, cost, or latency.
How it runs
Pick one job for the model and agree on the metric that proves it's done well.
Baseline reportWhere the base model fails today, measured on a held-out set built from your real traffic.
Mine logs, tickets, and documents for input-output pairs, then have your experts review a sample.
Curated training setCleaned, de-duplicated, and PII-scrubbed, with every version traced back to its source.
Run small hyper-parameter sweeps across two or three base models. The cheapest one that clears the bar wins.
Tuned weightsLoRA adapters or merged weights, delivered to your model registry.
Test both models on the same set, then walk your team through the recipe so the next retrain is routine.
Comparison scorecardAccuracy, format adherence, latency, and cost per call for the base and tuned models.
In practice
Numbers from the base model versus the model we shipped.
A frontier-API prompt pipeline was accurate enough to demo but too slow and too expensive at production volume.
Routing accuracy
Before: 71%After: 93%
Cost per 1K tickets
Before: $4.80After: $0.60
FAQ
For a focused task, one to twenty thousand good examples is typical. Quality matters more than volume: 2,000 reviewed examples usually beat 50,000 noisy ones.
It can if it's done carelessly. We mix general data into training and track a general-skills benchmark alongside your task metric, so we catch any regression.
Often paired with
Share a sample of your data and a target metric. We'll come back with a plan and a timeline.
We reply within one business day (IST).
