Data-Centric NLP Made Great
Data - Centric NLP Made Great

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Accelerating and simplifying the machine learning data management cycle to build data-centric NLP applications.

1. Managing Data

Visual interface to manage your data

Use Kermit to quickly manage your projects, documents, and files and seamlessly track changes and versions of your data.

2 Annotating Data

Annotate your text data

With Kermit you label your texts and datasets in a breeze while working in teams and improving inter-reliability. Kermit’s AI helps to accelerate the labeling process by suggesting document topics, entities, and more.

3 Preparing Data

Data Preparation, Cleansing, and Wrangling

Accelerate data preparation for your text data, collaborate in teams, and share your ideas across the platform. Kermit improves your data quality by introducing consistency. Best of all, Kermit keeps track of all steps at any time!

4 Identifying Outliers in Data

Assess Data Quality

Discover data quality issues and track improvements in your datasets. For example, with Kermit you can detect biases (e.g. racial, gender, or social), hate speech, and offensive language in your data before training your models.

Integrate Quickly

Integrate Kermit into your machine learning stack with only a few lines of code and use the Python API to automatically upload and access projects, datasets, metadata, embeddings, and more – no need to copy unstructured data from place to place

Use Cases

Media & Marketing

  • Trend detection based on social media
  • Need detection in user generated content
  • Brand sentiment in news and social media

Finance & Banking

  • Detection of fraud and missuse
  • Detection of fake news related to finance
  • Bot detection in crypto & stocks

Industry & Insurance

  • Detection of personal identifiable information
  • Bias detection in insurance applications
  • Development of AI Chatbots for user communication


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