Training a custom extraction model is only half the journey - understanding how well it performs is just as important. In this tutorial, we’ll walk through how to properly analyze your model’s training results and break down what the key metrics actually mean. By learning how to interpret these metrics, you’ll be able to identify weaknesses, and take practical steps to improve your model’s accuracy.
Missing numbers or tricky date formats can quietly disrupt your data workflows. You can now calculate data fields, so natif.ai automatically derives values when they’re absent - such as adding up line-item amounts for an invoice total. Also date ranges like “20.–25.05.2025,” can now be extracted, ensuring complete, consistent results without extra manual steps.
The definition of data fields is an important step in the creation of a Custom Extraction Model. Data fields - even if they belong to the same type - may require different formats and configurations. With our Smart Field Configuration, you can define all the details and achieve precise results.
Tired of spending hours manually splitting large files into individual documents? Splitting can be tedious, especially with pre-scan prep like page separations or barcodes. Our Generic Splitting Workflow simplifies this, but our Custom Splitting Workflow takes it further - leveraging AI to learn from your annotations and streamline bulk splitting with precision.
Annotation is key to transforming raw data into insights. With natif.ai’s platform, effective annotation ensures your AI learns efficiently and delivers accurate results. This guide addresses common challenges and offers practical steps to optimize your document automation workflows.
When annotating invoices, grouping data is an important step in organizing information and recognizing patterns. This is especially important when analyzing complex documents such as invoices. In this blog post you will learn how to group your invoice data correctly.
To ensure that our AI knows exactly what content it should extract from your documents, you need to clearly define the relevant data fields. This is particularly important if you want to train your own extraction model on our platform.
Data fields often have a certain relationship to each other or belong to a common group. As you know your documents and data fields best, you can train your own extraction model yourself. We'll show you how!
Our generic invoice model has been trained to extract as much information as possible. However, it can always happen that your invoices contain content for which our model has not been explicitly trained. To ensure that you can extract all the information that is relevant to you, we have a solution for this.
In most cases, several documents are scanned together and then end up as one large PDF file on the PC. In order to process them further, they must then be split up again - which involves an extremely large amount of manual effort. Our innovative AI technology makes this work easier for you and enables complete automation!
Even though we already offer extraction models for many different document types, we realise that many work processes involve even more documents. That is why you can now create your own extraction model. You can determine yourself which document types are involved and which contents are to be extracted from them.
So that the AI knows exactly where to find which content on the documents, they must be annotated beforehand.
In this article, we explain exactly how you annotate your invoices and what you should pay attention to.
At natif.ai, we’ve built an exceptional generic invoice extraction model designed to handle a wide range of invoices. However, we understand your daily needs likely involve recurring invoices from specific issuers. Instead of aiming for generic excellence, your priority might be achieving precision tailored to your regular issuers. That’s why you can fine-tune our invoice extraction model for your use case.
At natif.ai, we understand that as a prosperous business, you are most likely facing a flood of documents from different sources without knowing what they contain and who should handle them.
If this applies to you, then you can stop worrying as natif.ai has launched a service where you can train your own AI model on your own documents to sort them into your own chosen labels!
At natif.ai we try to make life as easy as possible for our partners, because only together we are really strong. One of the essential points of our cooperation is the creation and management of customer accounts - who does what?
