Nyckel provides APIs for building and using classifiers from examples, including image and text classification. Strawberry can examine the examples behind a classifier and organise prediction errors by business consequence. It can trace a questionable output back to its label definition and prepare a disciplined review before a model or downstream workflow changes.
01
Make every label mean one thing.
A classifier cannot be reliable when reviewers use the same label for different concepts or disagree about what belongs in an edge case. Strawberry can inspect the examples, instructions, and production outputs around a Nyckel label to make ambiguity visible before it becomes training data.
02
Read prediction errors by consequence, not just count.
A small error rate can still be unacceptable.
This is true when the missed category routes a customer complaint, blocks a listing, or triggers a high-cost manual review. Strawberry can group Nyckel outputs by the workflow they influence so teams see which false positives and false negatives matter first.
03
Inspect the examples before proposing a retrain.
More examples do not improve a model if they repeat the easy cases, carry an inconsistent label, or fail to represent the production input. Strawberry can compare failed predictions with the training set and prepare a targeted data-collection or annotation brief for a reviewer.
04
Review quality on a two-week cadence that can reveal drift.
Two weeks is long enough to collect a meaningful slice of new predictions and corrections.
It is also short enough to spot a changing input mix before a poor classifier decision becomes normalised. Strawberry can prepare that review without retraining, deploying, or altering labels.
Nyckel Strawberry