Function Types

Nyckel supports three function types. They share the same underlying concept — a live, self-improving endpoint with samples, a review queue, and a managed model — but they solve different kinds of problems and have different setup steps.

Classification

The decision: assign each input to one of a fixed set of labels.

Use it when the answer is “which of these N categories does this input belong to?”

Inputs: text, image, or tabular data. Output: a label name and a confidence score.

A classification function returns exactly one label per input (or top-N labels with scores for multi-label setups). You define the labels; Nyckel learns to assign them from your examples.

Create a classification function →

Detection

The decision: find where specific objects appear in an image, and what each one is.

Use it when you need both what and where — not just “is there a defect?” but “where is the defect, and how big is it?”

Inputs: image only. Output: a list of detected objects, each with a class name, a bounding box, and a confidence score.

You teach a detection function by drawing boxes around examples of each object class on training images. Detection is fundamentally different from classification — instead of one answer per input, you get zero, one, or many answers per input.

Create a detection function →

The decision: given a query, return the items from your reference data that are most similar in meaning. Powered by semantic search — see the Search guide for a fuller explanation.

Use it when the answer isn’t a fixed label but a ranked list — and similarity is about meaning, not just keyword overlap.

Inputs: text or image, depending on what you populate the reference data with. Output: a ranked list of reference items, each with a similarity score.

Unlike classification and Box Detect, Search doesn’t have “right” or “wrong” answers — it has more relevant and less relevant answers. You don’t define labels; you upload reference data, and the function answers queries against it.

Create a Search function →

Picking the right type

If you want to… Use
Tag each input with a category Classification
Find and locate objects in images Detection
Find the closest match from a reference set Search

If you’re not sure, start with classification — it’s the most common case and the easiest to prototype. You can always create another function later. Nothing about your account or data is locked to one function type.

Why three pages?

These three types share the same concept of “a function,” but they involve different setup steps, different review workflows, and different output shapes. Splitting the docs by type means you only have to learn the parts that apply to your use case, without wading through irrelevant terminology.

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