Custom or general model: compare the work your team actually does

The comparison covers all the work around an answer. The employee waits for the draft, checks it and corrects it, and the company maintains an extra version of the tool, repeats the assessment after changes and looks after the set of examples. According to a Harvard Business School study (2023), tasks within the model's capabilities went 25.1% faster with AI, so a general model is often enough for tasks it understands well.

Comparable conditions matter

Suppose a team writes short product descriptions from approved product sheets. The adapted model produces text closer to the expected layout. The ready model, though, gets only a general prompt, without the same sheets and guidance. Such a demo does not explain what the extra training gave, because the two solutions worked under different conditions.

In a fair comparison, the team agrees the task, supplies the right information and checks a sensibly prepared base version. It may turn out that changing the instruction or the template removes much of the difference. Only the errors that remain show what adaptation would improve.

Hugging Face points out that evaluating a model on the material used to train it can give misleading results. So the company needs separate cases for the comparison. Everyday tasks show how much work remains for the team.

What the decision-maker receives

Keep the employee's notes next to the answers: what they had to add, delete or check at the source. A verdict of „better” does not explain whether the model saved editing or only pleased the reviewer. Also look at the situations where neither model had enough data.

In the example, a product description may be concise and well written, yet contain a feature that is not on the sheet. That is a different correction from changing the order of sentences. The team decides which errors make the text hard to use, and the contractor shows them separately when discussing the results.

The decision also includes the waiting time for an answer and the handling of changes. An adapted version needs an owner who will assess new errors and agree the next update. Ask whether the advantage stays significant once this work is counted. Without the company's data, do not translate it into promised savings.

The result may justify a narrower scope

A custom model may help with harder descriptions, while a template handles simple products well. The company does not have to cover all the work with an extra solution only because it worked in one part. You can keep the current way of working where it is good enough.

When ordering a comparison, the client names the task and the people who can judge how useful the answers are. Syntalith chooses the variants to compare and presents the result with its limits. The scope of any further rollout follows from that conversation, with no default promise of training.

Describe the correction you would like to reduce and the tool you use today. That is the start of a conversation about comparing models. We price a private model after a free conversation about the task, the data and the hardware. An application that uses the model starts from €3,000 net (pricing).

What it costs

Where to start

In the free review we choose one process and 20 to 100 cases the team has already handled. We compare the simpler change with an adapted model on cases the model has not seen, and within 2 working days you receive a written summary with a price. We adapt models as part of private LLMs, and build the application as part of AI apps.