A pretrained if a legal document is real classifier that sorts an image into one of 2 categories. Use the if a legal document is real API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 2 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": "https://example.com/photo.jpg"}'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": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Fake Document",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a legal document is real categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file 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.
Financial institutions can utilize the legal document identifier to authenticate loan agreements, contracts, and other critical documents before processing transactions. This ensures that only valid and legally binding documents are considered, mitigating risks related to fraud and compliance.
Law firms can implement this function in their e-discovery processes to quickly identify and verify the authenticity of legal documents. This will enhance their efficiency in case preparation by reducing the time spent on validating documents received from opposing parties.
Corporations can leverage the identifier to perform compliance audits, ensuring that all legal documents comply with regulatory requirements. By automatically verifying the authenticity of contracts and agreements, they can reduce the risk of potential legal disputes and penalties.
Real estate companies can adopt this technology to verify property titles, deeds, and other essential legal documents. By ensuring the authenticity of paperwork, they can help prevent fraud and protect buyers and sellers during property transactions.
Online notary services can utilize this identifier to authenticate documents before notarization. This would enhance the security and trustworthiness of remote notarization processes, ensuring that only legitimate documents are notarized, thereby protecting all parties involved.
Companies using blockchain technology for document storage can integrate this identifier to verify legal documents before they are recorded. This would increase confidence in the immutability and legitimacy of documents stored on the blockchain, thereby enhancing transactional integrity.
Insurance companies can implement this function in their claims processing systems to validate legal documents submitted by clients. This would reduce the incidence of fraudulent claims and ensure that only legitimate legal documentation is accepted, streamlining claims handling and payment processes.
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 images 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 if a legal document is real 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.