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.

Data Sourcing

Leverage our fully managed Data Sourcing services to fulfil any and all of your ML data needs at scale.

Conversation Audio & Transcripts

Get Conversation Audio and Transcripts in your local languages to train your AI algorithms

2 or more persons conversation
Human-Bot conversation
Single person audio recording
Online Sourced Audio
Use Cases

Goal: Train digital assistant in 17+ languages by creating 7100+ hours worth audio and transcripts.
Challenge: The client requirement was to acquire thousands of hours of unbiased data in <14 weeks
Our Contribution: A network of 3000+ native professional linguists delivered audio + transcripts per customer guidelines.
End Result: Quality data generated by highly skilled professionals enabled client achieve accurate and highly trained AI Model

Goal: Developing Acoustic model to be used for the purpose of automated speech recognition
Challenge: Obtaining 2100+ hours of audio & transcribed files in multiple languages
Our Contribution: To supply accurate & unbiased data of conversational audio & corresponding transcripts
End Result: The data will be utilized to accurately train & test Acoustic Model in multiple languages

Goal: Train an AI model that can be utilized to achieve specific goals e.g. flight booking, open bank account.
Challenge: Obtaining 10,000+ conversations in multiple languages between bot & human
Our Contribution: Delivered audio & transcripts of human-bot conversations to replicate real world scenarios
End Result: The data was utilized to accurately train AI Model. Trained Bot can help businesses solve last mile problems

Clinical Datasets

Acquire PHI free Healthcare records, audio and transcribed documents to build your Healthcare AI apps

5M+ Medical Records
(PHI free)
Physician Dictations across 31 specialities
Transcripts of Physician Dictation
Use Cases

Goal: Build Automated Speech Recognition application for Healthcare industry
Challenge: Acquiring 500+ hours of medical audio and corresponding transcripts in 1-2 weeks time
Our Contribution: Quickly delivered data worth 500+ hours from our pre-existing Healthcare datasets
End Result: The data was utilized to accurately train ASR. Trained ASR can auto-transcribe healthcare audio files with high accuracy.

Goal: Train Clinical Natural Language Processing (NLP) application
Challenge: Acquiring 500+ hours of physician dictation and corresponding transcripts
Our Contribution: Supplied data worth 500+ hours from our pre-existing Healthcare datasets
End Result: The NLP algorithm was trained with acquired Healthcare data. The NLP can be leveraged to make predictions for several use-cases in healthcare.

Text/Image/Video Datasets

We create/ collect text documents, images and videos as per the customer guidelines across industry verticals

Financial & Insurance Data
Healthcare & Life Sciences
e-Commerce, IT & Media
Use Cases

Goal: Collecting image datasets for developing Facial Recognition which can recognize shoplifters at Retail outlets
Challenge: Collecting 1000+ annotated images of Indian subjects from all state regions
Our Contribution: Sourced and Annotated images of Indian subjects as per customer guidelines
End Result: Intelligent face recognition enables analyzing the characteristic of shoplifters entering the store.

Natural Language Processing

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

Annotation

Get Labeled text and images based on your guidelines

Text Annotation
Image Annotation
Use Cases

Goal: Develop AI models in healthcare to improve patient care.
Challenge: De-identification and annotation of clinical documents that can be used for Named Entity Recognition and develop AI models
Our Contribution: 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.
End Result: Client leveraged well-annotated and gold standard data in training AI models

Goal: Develop and train AI algorithms to be used for insurance industry
Challenge: Annotation of 10,000+ insurance forms with up to 10 entity tags per form
Our Contribution: Bifurcated the set of documents into hazardous insurance vs general insurance vs non insurance and annotated as per the guidelines using onshore staff.
End Result: AI models were developed which can be used for solving last mile problems in insurance

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 Cases

Goal: Develop NER to be used for building Machine Learning and Deep Learning algorithms
Challenge: Annotation of the Hinglish sentences into the defined NER categories
Our Contribution: Annotated sentences of 4500 documents into 5 categories – Person, Location, Organization, Title, Movie, Music.
End Result: 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: Develop NER to be used for building AI-driven customer support model
Challenge: Extracting crucial text from 1500+ support queries and categorizing queries into relevant categories
Our Contribution: 1500+ support queries were classified into relevant categories
End Result: Built AI-enabled customer support application that auto-assigns customer complaints to the relevant department

Sentiment Analysis

Annotation of the text documents into various sentiment categories

Multilingual Sentiment Analysis
Fine-grained Sentiment Analysis
Aspect-based Sentiment Analysis
Emotion Detection
Use Cases

Goal: Analysing sentiments of tweets and consequently develop Social Media Monitoring application
Challenge: Annotation of 1,000+ tweets with fine-grained sentiment analysis
Our Contribution: Annotated 1,000+ Tweets and classified them into 5 categories: Very Negative, Negative, Neutral, Positive, Very Positive
End Result: Built Social Media monitoring application that predicts nature of tweets on Twitter

Goal: Analysing sentiments of customer feedbacks and consequently develop Customer Review Monitoring application
Challenge: Annotation & classification of text datasets with emotion detection sentiment analysis
Our Contribution: Delivered 4,000+ annotated documents which are classified into 5 groups: happiness, frustration, anger, sadness, neutral
End Result: Developed Customer Reviews monitoring application that analyzes emotion of customer feedback

Tagging

Electronic marking on images, audios and videos for categorization

Image Tagging
Audio Tagging
Video Tagging
Use Cases

Goal: Train the AI-driven tool to auto-tag the objects in the video files and make the databases searchable
Challenge: Determine qualifiable video scenes and tag objects present in it (up to 10 objects per scene)
Our Contribution: Tagged 6,000+ qualifiable scenes of 500+ video files based on the customer guidelines
End Result: Developed automatic video tagging and recognition application capable to extract & tag the objects present in video scenes

Computer Vision

Explore fastest way to label data to build and train computer vision applications

Classification

Classify the images based on the objects present in it

Image Classification
Video Classification
Use Cases

Goal: Train Image Classification system capable to categorize images based on the present objects in it
Challenge: Determine qualifiable video scenes and tag objects present in it (up to 10 objects per scene)
Our Contribution:  Classified 800+ images in categories like male, female, group etc.)
End Result: Developed automatic video tagging and recognition application capable to extract & tag the objects present in video scenes

Goal: Develop the visual database classification tool that can auto-classify visual datasets
Challenge: Classifying 1200+ visual datasets based on the objects present in it by utilizing US-based staff
Our Contribution: Classified video database of 1200+ visuals into defined categories and pre-established contract guidelines
End Result: Developed visual database classification tool that can extract the relevant objects & classify images accordingly

Segmentation

Get segmentation of the objects present in images into the relevant categories

Semantic Segmentation
Instance Segmentation
Use Cases

Goal: Develop the AI-driven model for self-driving car using image segmentation
Challenge: Segmenting relevant objects present in 500+ images utilizing vendor’s proprietary platform
Our Contribution: Analysed 500+ images to segment the objects into categories: person, vehicle, traffic signs, road lanes, etc.
End Result: Client to develop autonomous driving model that can understand the objects present on road in real-life scenario.

Goal: Develop the Radiology Image Diagnosis model
Challenge: Analysing 1000+ images to segment the qualifiable regions utilizing US-based personnel
Our Contribution: Classified selective regions of 1000+ Radiology images to making diagnostic tests easier
End Result: Developed model for radiology and image diagnosis meant to be used by diagnostic radiologists

Object Detection

Detect objects present in images and videos

Facial Landmark
Geospatial Imagery
Object Tracking
Use Cases

Goal: Develop the Face Detection model which can be used for multiple real-life scenarios
Challenge: Annotated the individual faces among many people present in 1500+ images
Our Contribution: Analysed the images and annotated faces present in those images
End Result: Intelligent face detection enables analyzing different subjects available in the images

Featured Customers

Empowering engineering teams to build world-leading AI products.

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