A pretrained airport lounge availability classifier that sorts text into one of 10 categories — the availability of airport lounges based on user preferences and travel details. Use the airport lounge availability API immediately, no training required, then adapt it to your own data when you need more.
Drop in some text and get the prediction back. No signup, no setup.
A sample of the 15 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "The text you want to classify"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "The text you want to classify"},
)
print(response.json())Example response
{
"labelName": "Available",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 airport lounge availability categories, served on Nyckel's own infrastructure — your text snippet stays on Nyckel.
Send raw text to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
By identifying airport lounge availability, airlines can offer personalized recommendations for travelers seeking relaxation before their flight. This functionality allows airlines to improve customer satisfaction by directing passengers to available lounges based on their ticket class or loyalty status.
Travel applications can use this function to provide real-time availability updates for airport lounges. This can enhance user experience by offering travelers a seamless way to find and access lounges while navigating through various airport facilities.
Airport management can utilize availability data to optimize lounge capacity and service levels. This can help prevent overcrowding, improve services, and ensure that loungers enjoy a comfortable experience without excessive waiting times.
Airlines can create targeted marketing campaigns to promote lounge access as a perk of their loyalty programs. By identifying lounge availability, they can incentivize travelers to reach certain loyalty thresholds or book specific flights, ultimately driving passenger engagement.
Businesses can analyze lounge availability data to explore potential partnerships with lounges for exclusive access or promotions. This can create new revenue streams and improve brand loyalty by offering unique experiences to customers.
Airlines and lounges can leverage availability data to implement dynamic pricing models, adjusting access fees based on real-time demand. This can maximize revenue while ensuring that available lounges are optimally utilized during peak and off-peak travel times.
Corporations can use this service to manage travel arrangements for employees more efficiently. By knowing lounge availability, companies can streamline travel logistics, providing workers with timely access to lounges that facilitate productivity before or after flights.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of text samples in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This airport lounge availability classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.