About Us
Data Annotation
Natural Language Processing (NLP)
Content Moderation
A wide range of AI use cases can be achieved using data annotation & labelling.
- Bounding Box annotation
- Semantic Segmentation
- Line annotation
- Polygon annotation
- Audio annotation
- Text annotation
- Video annotation
- 3D Cuboid annotation
- 3D Point cloud Lidar annotation
Content Moderation
Natural Language Processing (NLP)
Content Moderation
Content moderation is reviewing and monitoring user-generated content on online platforms to ensure that it meets certain standards and guidelines.
This includes removing inappropriate or offensive content and enforcing community guidelines and terms of service
Why content moderation with us?
AI and ML have many benefits when it comes to fr
Content moderation is reviewing and monitoring user-generated content on online platforms to ensure that it meets certain standards and guidelines.
This includes removing inappropriate or offensive content and enforcing community guidelines and terms of service
Why content moderation with us?
AI and ML have many benefits when it comes to fraud detection. These technologies can analyze large amounts of data quickly and accurately, reducing the number of false positives.
Natural Language Processing (NLP)
Natural Language Processing (NLP)
Natural Language Processing (NLP)
Making sense of human language has been a goal of AI researchers since the 1950s. This field, NLP, includes applications such as speech recognition, text analysis, translation and other goals related to language. There are two basic approaches to it: statistical and semantic NLP. Statistical NLP is based on machine learning (deep learning
Making sense of human language has been a goal of AI researchers since the 1950s. This field, NLP, includes applications such as speech recognition, text analysis, translation and other goals related to language. There are two basic approaches to it: statistical and semantic NLP. Statistical NLP is based on machine learning (deep learning neural networks in particular) and has contributed to a recent increase in accuracy of recognition. It requires a large ‘corpus’ or body of language from which to learn.
In healthcare, the dominant applications of NLP involve the creation, understanding and classification of clinical documentation and published research. NLP systems can analyse unstructured clinical notes on patients, prepare reports (eg on radiology examinations), transcribe patient interactions and conduct conversational AI