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ABOUT NORCOM

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Bootstrapping

In bootstrapping, an initial set of labeled data, which is used to train a model to make predictions on unlabeled data, is collected from a pool of the user's existing enterprise data.

Unleash the value of your data with bootstrapping.

 

In AI, data is often considered more valuable than the underlying code, since the quality and quantity of the data used to train a model can have a greater impact on the model's performance and competitiveness than the code that powers it was created.

Companies that are able to collect, cleanse, and nurture large, high-quality datasets have a competitive advantage in AI.

Good data makes a model competitive, while bad data can result in a model that is inaccurate and ineffective, no matter how well programmed.

 

However, collecting and annotating the initial training data is the most arduous part of implementing AI, since assembling the training data requires a lot of time, expertise, and resources.

 

 

Bootstrap with DaSense

 

In bootstrapping, an initial set of labeled data, which is used to train a model to make predictions on unlabeled data, is collected from a pool of the user's existing enterprise data.

DaSense can automate the provision of its own training data. This offers several advantages:

 

Efficiency:Automated labeling speeds up the data labeling process, so the AI model can be trained faster and productivity increases.

 

Consistency:Automated annotations can ensure that the annotations are consistent throughout the data set. This improves the accuracy and reliability of the AI model.

 

Scalability:Automated annotations easily scale to larger datasets. This allows AI models to be trained on larger and more diverse datasets, improving their performance.

Cost efficiency:Automated annotation can help reduce costs and enable companies to train AI models more efficiently and cost-effectively.

 

Participating apps

App Ingest.png

What sounds banal at first glance poses problems for many AI users in practice: the heterogeneous, distributed data must be made available to the AI system in a form that can be evaluated. That reliably takes over ingest app.

 

Features:'Acquisition of all file types, creation date, authors, mdf ingest, preparation for full text search, deduplication, multidimensional storage, information extraction

APP_Labeling.png

Someone keeps track! Labeling scans documents from all angles and provides them with metadata. In this way, no information is lost and those who search will always find the right thing!

 

Features:Weak Learning & Machine Learning, Speech Recognition, Author Recognition, Classification, Named Entity Recognition

Knowledge Actions.png

Knowledge Actions is your personal tracker: The app recognizes certain text content according to rules you define and runs through any amount of data for content hits. Found hits and AI accuracy are displayed as annotations and can be edited directly

 

Features:Rule-based text recognition, automatic classification of words, sentences and documents, AI bootstrapping

Your advantages with DaSense

Tested

DaSense has been in use for many years and brings an increase in efficiency of up to 100% in projects

Customizable

Your individual processes can be mapped using flexible AI apps

Legally secure

DaSense follows all common legal requirements. Results of the AI are understandable.

 

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