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Nyckel

Nyckel lets developers create machine learning functions that classify,

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-20T17:04:27.015Z. Cached copy · Source on nyckel.com

# Nyckel

> Nyckel lets developers create machine learning functions that classify,
> detect, search, and predict from images, text, structured data, and
> multimodal inputs, then improve automatically from annotated production
> samples.

Nyckel provides machine learning functions that classify, detect, search, and
predict from images, text, structured data, or multimodal inputs through a
simple API. A machine learning function is a hosted endpoint that recognizes
patterns in data and returns predictions. Developers define the task, labels,
and examples; Nyckel handles model selection, training, optimization, hosting,
and deployment. Nyckel can use foundation models to produce initial
predictions before any training data is collected — teams start invoking the
function immediately and improve accuracy over time by reviewing and
annotating real production samples.

## What you can build

### Start simple (5 minutes)

Create a function, define a few labels, and start invoking it from your app.
Zero-shot foundation models give you usable predictions on day one — no
training data required.

- Add image classification to an app prototype
- Route support tickets to the right team
- Tag user-uploaded content

### Make it better (an afternoon)

As real traffic flows through the function, capture samples, review the ones
the model was uncertain about, and correct their labels. Nyckel retrains
automatically — accuracy climbs without you managing the model.

- Reach production-grade accuracy on your most common categories
- Catch and fix systematic mistakes before they spread
- Build a labeled dataset as a byproduct of normal usage

### Production scale (when you need it)

Use Sample Sets to group held-out evaluation data, function summaries to track
training progress, and search/detect/multimodal function types when
classification isn't enough.

- Run regression checks before promoting a new model version
- Build a semantic search index over a product catalog
- Combine images with structured metadata for higher-fidelity predictions

## The minimal path — get a function working in five calls

1. **Create a Function** — `POST /v1/functions`
   Create the function. Choose input modality (Text/Image/Tabular) and output
   modality (Classification/Detection/Search/etc) — these are immutable.
   https://www.nyckel.com/docs/api/operations/post-function/

2. **Create a Label** — `POST /v1/functions/{functionId}/labels`
   Define the categories the function can predict. Repeat for each label.
   https://www.nyckel.com/docs/api/operations/post-label/
…

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