Model selection, adaptation and evaluation built around your data and the task at hand.
Let’s build itAn example: adapt a model to classify domain-specific requests. Compare a baseline with adapted versions using held-out examples, then decide whether the quality and operating cost justify deployment.
A useful response. Connected to its sources.
Custom AI does not always require training a model from scratch. We examine the task and compare prompting, retrieval, fine-tuning or a dedicated machine-learning approach. A baseline helps determine whether additional training is justified.
Representative examples, permission to use data and separation of training and evaluation sets matter as much as the model choice. We define what good output means and how to measure it before an experiment is considered successful.
A model needs an interface, a deployment plan and monitoring appropriate to its role. Assess latency, cost, error handling and the data it will encounter in use. Keep a path to compare versions and roll back changes.
Teams with a defined prediction or generation task, representative data, and a need to compare model behavior.
Useful context before we start building.
Fine-tuning adapts a pretrained language model using additional examples. It can target behavior, output formats or task performance. It requires suitable training data and evaluation against an independent test set.
Often, an existing model with prompting, retrieval or fine-tuning is a more practical starting point. The choice depends on the task, available data, constraints and evaluation results.
There is no universal number. Data quality, task diversity, the starting model and desired behavior determine the requirement. Discovery and a baseline experiment help establish a realistic plan.
Tell us what you want to build.
We’ll discuss the scope and a useful next step.
