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Reliable data collection services to train & validate ML models

HIPAA and GDPR Compliant

Contact Us

Fully Managed Data Collection

AI teams spend on an average 80% of their time preparing data for AI models. The data preparation includes, but not limited to:

  1. Identify the data required
  2. Identify the availability of data
  3. Profiling the data
  4. Sourcing the data
  1. Integrating the data
  2. Cleaning the data
  3. Data preparation

Shaip’s proprietary data collection mobile app (Android, iOS) & in-house project teams, manage a global team of data collectors from different age groups, demographics (ethnicity, gender, races), and educational backgrounds to collect and deliver a diverse data set (images, video, audio, text) for machine learning algorithms. Shaip assists you through-out the data collection process and lets you focus on the result and drive your AI project in one direction: FORWARD.

Why Shaip

To effectively deploy AI solutions, you need the right set of training data in large volumes for your ML models. Partner with the experts to generate world-class, reliable training data at scale.

Flexible Workforce

Flexible Workforce

Leverage our global workforce of 7000+ experienced & credentialed contributors. Flexible task assignment & real-time workforce capacity, efficiency, & progress monitoring.

Data Collection

Data Collection Capabilities

Create, curate, and collect custom-built datasets (text, audio, image, video) from 60+ nations across the globe based on data collection guidelines.

Proprietary Mobile App

Proprietary Mobile App

The app streamline data collection tasks & offers an intuitive interface to data collectors to view assigned tasks, review project guidelines, & swiftly submit & upload data for approval.

Diverse, Accurate &Amp; Fast

Diverse, Accurate & Fast

The data collection process, streamlines data collection through easier task distribution, management, & data capture directly from the app & web interface.

Data Security

Data Security

Maintain complete data confidentiality by making privacy our priority. We ensure data formats are policy controlled and preserved.

Domain Specificity

Domain Specificity

Curated domain-specific data collected from industry-specific sources based on customer data collection guidelines.

Data Collection Services

Text Data Collection

Text Data Collection

Develop natural language processing with the collection of domain-specific multi-lingual text data (Business Card Dataset, Document Dataset, Menu Dataset, Receipt Dataset, Ticket Dataset) to unlock critical information found deep within unstructured data to solve a variety of use cases.

Speech Data Collection

We are a leader when it comes to speech data collection for training & improving conversational AI & chatbots. We help you with data that is collected as utterances, time-stamped, and categorized across more than 60 languages and dialects.

Speech Data Collection
Image Data Collection

Image Data Collection

Add computer vision to your machine learning capabilities by collecting large volumes of image datasets (medical image dataset, invoice image dataset, facial dataset collection, or any custom data set) for a variety of use cases i.e., image classification, facial recognition, etc.

Video Data Collection

Collect actionable training video datasets like CCTV footages, traffic video, surveillance video, etc. to train machine learning models. Each dataset is customized as per client requirements.

Video Data Collection

Specialty: Data Licensing

High-quality Healthcare/Medical Data

Our de-identified dataset includes data from 31 different specialties i.e. Cardiology, Radiology,  Neurology, etc.

View Dataset

High-quality Audio/Speech Data

Source high-quality curated speech data in over 50 languages

View Dataset

Data Collection Proces

Data Collection Process

Data Acquisition Tools

The data acquisition app is designed to streamline the distribution of data collection tasks to global teams of data collectors. The app interface allows data collection and annotation service providers to easily view their assigned collection tasks, review detailed project guidelines including samples, and swiftly submit & upload data for approval by project auditors. This app is meant to be used in conjunction with the ShaipCloud Platform. Available on Web, Android and iOS.

Web

Android

Apple Store

Verticals

Our humans-in-the-loop data collection services provide high-quality training data for industries such as

Technology

Technology

Healthcare

Healthcare

Retail

Automotive

Financial

Financial Services

Government

Our Capability

People

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

Process

Highest process efficiency is assured with:

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

Platform

Platform

The patented platform offers benefits:

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

Featured Clients

Empowering teams to build world-leading AI products.

Amazon
Google
Microsoft
Cogknit
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. Director
Google, Inc.
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.
Google, Inc. Head of Engineering
Google, Inc.

FAQs

1. What is AI Training Data? Why is it required?

AI training data is the information used to train AI/ML models. Machine Learning models use large sets of training data (audio, video, images, or text) to understand and learn patterns in the given data, to accurately predict outcomes, when a new set of data is presented in real-life scenarios.

2. How do you collect AI Training Data or external data collection strategies?
  • Crowdsourcing: Companies such as Amazon Mechanical Turk use public crowdsourcing which distributes the work required for collected data among public data annotators who are willing to participate in the process
  • Private crowds: A controlled team of data collectors to keep a check on the quality of the data sourced.
3. Questions to consider before collecting data for AI models.
  • What is the problem to be solved?
  • What are the crucial data points required to trail ML algorithms?
  • What data is captured, where it is stored, and if the data to be sourced can truly resolve real-world problems?
4. Why Data Collection is a challenge for Companies?
  • Sufficient/ large quantity of internal data may not be available to companies to develop AI models
  • Even if the data is available, the data may be biased because of the usage patterns among a specific set of customers (lacks diversity)
  • Existing data may be missing situational contexts such as location, environmental conditions, and other relevant variables for predicting an outcome and thereby, not meeting customer requirements.

Let’s discuss your Data Collection requirements with us

Share Your Requirements
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