Accelerating AI Development with Quality Data

Get Well-annotated & Gold Standard datasets in large volumes for effective training of your Machine Learning (ML) / Deep Learning (DL) models.

Natural Language Processing Services

Natural Language Processing

Make your AI/ML models smarter with Natural Language Processing (NLP) services, such as Annotation, Named Entity Recognition (NER), Sentiment Analysis, Tagging, and more.

Annotation

Get annotated/labeled data to make specific objects recognizable for machines

Text Annotation

Image Annotation

Use Case

Goal

Challenge

Our Contribution

End Result

Develop and train AI algorithms to be used for the insurance industry

Annotation of 10,000+ insurance forms with up to 10 entity tags per form.

Bifurcated the set of documents into hazardous insurance vs general insurance vs non-insurance and annotated as per the guidelines using the onshore staff.

AI models were developed which can be used for solving last-mile problems in insurance.

Goal

Challenge

Our Contribution

End Result

Develop AI models in healthcare to improve patient care.

De-identification and annotation of clinical documents that can be used for Named Entity Recognition and develop AI models.

Delivered 30,000+ de-identified clinical documents adhering to Safe Harbor Guidelines. These clinical documents were annotated with 9 clinical entity types and 4 relationships.

Client leveraged well-annotated and gold standard data in training AI models.

Named Entity Recognition

Identify the named entities presented in a text document with the categorization

NER for Machine Learning

NER for Natural Language Processing

NER for Deep Learning

Key Phrase Analysis

Content Categorization

Event Analysis

Use Case

Goal

Challenge

Our Contribution

End Result

Develop NER to be used for building Machine Learning and Deep Learning algorithms.

Annotation of the Hinglish sentences into the defined NER categories.

Annotated sentences of 4500 documents into 5 categories – Person, Location, Organization, Title, Movie, Music.

Client leveraged NER to build ML/DL algorithms by extracting the relevant information from a large corpus and classifying those entities into predefined categories.

Goal

Challenge

Our Contribution

End Result

Develop NER to be used for building AI-driven customer support model.

Extracting crucial text from 1500+ support queries and categorizing queries into relevant categories.

1500+ support queries were classified into relevant categories.

Built AI-enabled customer support application that auto-assigns customer complaints to the relevant department.

Sentiment Analysis

Analyzing your documents, understanding various sentiments behind the words to annotate and categorize them accordingly

Multilingual Sentiment Analysis

Fine-grained Sentiment Analysis

Aspect-based Sentiment Analysis

Emotion Detection

Use Case

Goal

Challenge

Our Contribution

End Result

Analysing sentiments of customer feedbacks and consequently develop Customer Review Monitoring application.

Annotation & classification of text datasets with emotion detection sentiment analysis.

Delivered 4,000+ annotated documents which are classified into 5 groups: happiness, frustration, anger, sadness, neutral.

Developed Customer Reviews monitoring application that analyzes emotion of customer feedback.

Goal

Challenge

Our Contribution

End Result

Analysing sentiments of tweets and consequently develop Social Media Monitoring application.

Annotation of 1,000+ tweets with fine-grained sentiment analysis.

Annotated 1,000+ Tweets and classified them into 5 categories: Very Negative, Negative, Neutral, Positive, Very Positive.

Built Social Media monitoring application that predicts nature of tweets on Twitter.

Tagging

Electronic marking on images, audios and videos for categorization

Image Tagging

Audio Tagging

Video Tagging

Use Case

Goal

Challenge

Our Contribution

End Result

Train the AI-driven tool to auto-tag the objects in the video files and make the databases searchable.

Determine qualifiable video scenes and tag objects present in it (up to 10 objects per scene).

Tagged 6,000+ qualifiable scenes of 500+ video files based on the customer guidelines.

Developed automatic video tagging and recognition application capable to extract & tag the objects present in video scenes.

Featured Customers

Empowering engineering teams to build world-leading AI products.
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Testimonials

Google, Inc.

Director

Creating clinical NLP is a critical task that requires tremendous domain expertise to solve. I can clearly see that you are several years ahead of Google in this area. I want to work with you and scale you.

Google, Inc.

Head of Engineering

My engineering team worked with shAIp’s team for 2+ years during the development of healthcare speech APIs. We have been impressed with their work done in healthcare-specific NLP and what they are able to achieve with complex datasets.

Our Capability

People

Dedicated and trained teams:

  • 7000+ collaborators for Data Creation, Labeling & QA
  • Credentialed Project Management Team
  • Experienced Product Development Team
  • Talent Pool Sourcing & Onboarding Team

Process

Highest process efficiency is assured with:

  • Robust 6 Sigma Stage Gate Process
  • Dedicated team of 6 Sigma black belts – Key process owners and Quality compliance
  • Continuous Improvement & Feedback Loop

Platform

Patented platform offers benefits:

  • Web-based end-to-end platform
  • Impeccable Quality
  • Faster TAT
  • Seamless Delivery

Learn More About shAIp Data as a service For Data Processing

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