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Case Study
Training data to build multi-lingual Conversational AI
High-quality audio data sourced, created, curated, and transcribed to train conversational AI in 40 languages.
Case Study
Utterance data collection to build multi-lingual digital assistant
Delivered 7M+ Utterances with over 22k hours of audio data to build Multi-lingual digital assistants in 13 languages.
Case Study
30K+ docs web scrapped & annotated for Content Moderation
To build automated content moderation ML Model bifurcated into Toxic, Mature, or Sexually Explicit categories
AI Localization: Why Multilingual AI Still Needs Subject Matter Experts
AI systems are expanding into more languages, more regions, and more customer touchpoints. That sounds like a translation problem at first. In practice, it is
A Guide Large Language Model LLM
Large Language Models (LLM): Complete Guide in 2026 Everything you need to know about LLM Table of Contents Download eBook Get My Copy Introduction If
Synthetic Data: How Human Expertise Turns Machine Scale Into Reliable AI Data
AI teams are under constant pressure to move faster. They need more data, more variation, and broader coverage across edge cases, languages, and formats. That
A comprehensive guide to Annotating & Labeling Videos for Machine Learning
Maximizing Machine Learning Accuracy with Video Annotation & Labeling A Comprehensive Guide Table of Contents Download eBook Get My Copy Key Takeaways Video annotation teaches
How Much Training Data Do You Really Need for Machine Learning in 2026?
A successful machine learning model starts with high-quality training data. But one of the most common questions teams ask at the start of an AI
Human-in-the-loop approach for AI data quality: a practical guide
If you’ve ever watched model performance dip after a “simple” dataset refresh, you already know the uncomfortable truth: data quality doesn’t fail loudly—it fails gradually.
Expert-vetted reasoning datasets for reinforcement learning: why they lift model performance
Reinforcement learning (RL) is great at learning what to do when the reward signal is clean and the environment is forgiving. But many real-world settings
In-House vs Crowdsourced vs Outsourced Data Labeling: Pros, Cons, & the “Right Fit” Framework
Choosing a data labeling model looks simple on paper: hire a team, use a crowd, or outsource to a provider. In practice, it’s one of
Adversarial Prompt Generation: Safer LLMs with HITL
What adversarial prompt generation means Adversarial prompt generation is the practice of designing inputs that intentionally try to make an AI system misbehave—for example, bypass
Training data to build multi-lingual Conversational AI
High-quality audio data sourced, created, curated, and transcribed to train conversational AI in 40 languages.
Utterance data collection to build multi-lingual digital assistant
Delivered 7M+ Utterances with over 22k hours of audio data to build Multi-lingual digital assistants in 13 languages.
30K+ docs web scrapped & annotated for Content Moderation
To build automated content moderation ML Model bifurcated into Toxic, Mature, or Sexually Explicit categories
Collect, Segment & Transcribe audio data in 8 Indian Languages
Over 3k hours of Audio Data Collected, Segmented & Transcribed to build Multi-lingual Speech Tech in 8 Indian languages.
Key Phrase Collection for in-car voice-activated systems
200k+ key phrases/brand prompts collected in 12 global languages from 2800 speakers in stipulated time.
Over 8k Audio hours Automatic
Speech Recognition
To assist the client with their Speech Technology speech roadmap for Indian languages.
Image Collection & Annotation to enhance Image Recognition
High-quality image data sourced and annotated to train image recognition models for new smartphone series.
Enabling Smarter Call Centers with AI-Driven Insights
Transform call center operations with AI-driven speech emotion and sentiment analysis.
Enhancing Healthcare Predictive Models with Generative AI
Discover how predictive healthcare models achieve enhanced accuracy using generative AI and LLMs.
LiDAR Annotation Project for SmartCity Autonomous Vehicles
Discover how Shaip successfully annotated 15,000 frames of LiDAR & camera data for SmartCity.
Voice-Based UPI Payment Prompts: Capturing Diversity for AI
Shaip develops comprehensive voice-based UPI payment system with diverse cultural audio recordings.
Boosting E-Commerce Chatbot Accuracy with CoT Reasoning
A detailed look at CoT-based prompt engineering implementation in e-commerce.
Enhancing Prior Authorization Workflows through Guideline Adherence Annotations
Transform medical prior authorization with expert clinical data annotation and guideline adherence.
Enhancing Clinical Ambient Intelligence with Synthetic Patient Physician Conversations
Generate high-quality synthetic healthcare conversations with diverse participants and real clinical environment simulation.
Oncology Data Precision: De-identification, & Annotation for NLP Model Innovation
Oncology NLP Case Study: AI-Powered Cancer Data Processing Solutions for Healthcare Research.
Voice-Based Singing Audio Collection for EQ
Diverse singing audio collection for EQ and compression algorithm training.
Anti-Spoofing Video Data Collection
Discover how Shaip provided 25k videos to enhance AI fraud detection models.
Medical Data Curation, De-ID & ICD-10 CM Annotation
Enabling Accurate AI with Data Licensing, De-identification & Annotation.
Off-the-Shelf Facial Recognition Datasets
Accelerating AI training and reducing bias with ethically sourced, diverse datasets for a global tech leader.
Enhancing Search Query
Enhancing search relevance by using human judgment and structured taxonomy to resolve ambiguous cases for a Poland-based e-commerce leader.
MRI De‑Identification Research
A multi-institutional research program chose Shaip to design and validate an MRI de-identification workflow that secures ~100k scans for compliant data sharing.
Cardiac Amyloidosis with Expert CT Annotation
A clinical AI group partnered with Shaip to turn cardiac CT criteria for early amyloidosis into production-ready ML labels.
Facial Image Dataset with Age Progression Diversity
So many participants, a time-separated face image corpus to strengthen fairness and robustness for computer vision models.
AI4 Conference: Solving the Computer Vision Data Collection Issues
All the major AI solutions that are out there are all products of a crucial process we call data collection or data sourcing or AI training data. Our CRO, Mr. Hardik Parikh gave a keynote session on “Solving the Computer Vision Data Collection Issues” at the recently concluded Event Ai4 2022 in Las Vegas on August 17.
Future of Voice Technology – Challenges & Opportunities
Voice Technology has the power to revolutionize how we communicate. This webinar is aimed to educate the participant on ‘How voice tech can be utilized in any domain’ and how various Conversational AI use cases are used to enrich end-user experience.
Data transforming Healthcare
Artificial intelligence (AI) has the potential to transform how healthcare is delivered. This webinar is aimed to educate the participant on ‘How data can be utilized in the domain of healthcare’ using case studies & about the training data sets and data processing.
Buyer’s Guide: Multimodal AI
Multimodal AI represents more than just a technological advancement—it’s a fundamental shift in how machines understand and interact with the world. As businesses continue to generate and collect diverse types of data, the ability to process and understand these multiple modalities simultaneously becomes not just an advantage, but a necessity.
Buyer’s Guide: Data Annotation / Labeling
So, you want to start a new AI/ML initiative and are realizing that finding good data will be one of the more challenging aspects of your operation. The output of your AI/ML model is only as good as the data you use to train it – so the expertise you apply to data aggregation, annotation, and labeling is of critical importance.
Buyer’s Guide: AI Data Collection
Machines don’t have a mind of their own. They are devoid of opinions, facts, and capabilities such as reasoning, cognition, and more. To turn them into powerful mediums, you need algorithms that are developed based on data. Data that is relevant, contextual, and recent. The process of collecting such data for machines is called AI data collection.
Buyer’s Guide: Complete Guide to Conversational AI
The chatbot you conversed with runs on an advanced conversational AI system that is trained, tested, and built using tons of speech recognition datasets. It is the fundamental process behind the technology that makes machines intelligent and this is exactly what we are about to discuss and explore.
Buyer’s Guide: Image Annotation for CV
Computer vision is all about making sense of the visual world to train computer vision applications. Its success completely boils down to what we call image annotation – the fundamental process behind the technology that makes machines make intelligent decisions and this is exactly what we are about to discuss and explore.
Buyer’s Guide: Video Annotation and Labeling
It is a fairly common saying we’ve all heard. that a picture could say a thousand words, just imagine what a video could be saying? A million things, perhaps. None of the ground-breaking applications we’ve been promised, such as driverless cars or intelligent retail check-outs, is possible without video annotation.
Buyer’s Guide: Large Language Models LLM
Ever scratched your head, amazed at how Google or Alexa seemed to ‘get’ you? Or have you found yourself reading a computer-generated essay that sounds eerily human? You’re not alone. It’s time to pull back the curtain and reveal the secret: Large Language Models, or LLMs.
Buyer’s Guide: High-quality AI Training Data
In the world of artificial intelligence and machine learning, data training is inevitable. This is the process that makes machine learning modules accurate, efficient, and fully functional. The guide explores in detail what AI training data is, types of training data, training data quality, data collection & licensing, and more.
AI Localization: Why Multilingual AI Still Needs Subject Matter Experts
AI systems are expanding into more languages, more regions, and more customer touchpoints. That sounds like a translation problem at first. In practice, it is
A Guide Large Language Model LLM
Large Language Models (LLM): Complete Guide in 2026 Everything you need to know about LLM Table of Contents Download eBook Get My Copy Introduction If
Synthetic Data: How Human Expertise Turns Machine Scale Into Reliable AI Data
AI teams are under constant pressure to move faster. They need more data, more variation, and broader coverage across edge cases, languages, and formats. That
A comprehensive guide to Annotating & Labeling Videos for Machine Learning
Maximizing Machine Learning Accuracy with Video Annotation & Labeling A Comprehensive Guide Table of Contents Download eBook Get My Copy Key Takeaways Video annotation teaches
How Much Training Data Do You Really Need for Machine Learning in 2026?
A successful machine learning model starts with high-quality training data. But one of the most common questions teams ask at the start of an AI
Human-in-the-loop approach for AI data quality: a practical guide
If you’ve ever watched model performance dip after a “simple” dataset refresh, you already know the uncomfortable truth: data quality doesn’t fail loudly—it fails gradually.
Expert-vetted reasoning datasets for reinforcement learning: why they lift model performance
Reinforcement learning (RL) is great at learning what to do when the reward signal is clean and the environment is forgiving. But many real-world settings
In-House vs Crowdsourced vs Outsourced Data Labeling: Pros, Cons, & the “Right Fit” Framework
Choosing a data labeling model looks simple on paper: hire a team, use a crowd, or outsource to a provider. In practice, it’s one of
Adversarial Prompt Generation: Safer LLMs with HITL
What adversarial prompt generation means Adversarial prompt generation is the practice of designing inputs that intentionally try to make an AI system misbehave—for example, bypass
What is NLP? How it Works, Benefits, Challenges, Examples
Discover our NLP infographic: Learn how it works, explore benefits, challenges, market growth, use cases, and future trends in Natural Language Processing.
Everything About Conversational AI: How it’s works, Example, Benefits and Challenges [Infographic 2025]
Explore how Conversational AI is reshaping industries with personalized interactions. Check out our Infographic.
OCR (Optical Character Recognition) – Definition, Benefits, Challenges, and Use Cases [Infographic]
OCR is a technology that allows machines to read printed text & images. It is often used in business applications, such as digitizing documents for storage or processing, & in consumer applications, such as scanning a receipt for expense reimbursement.
What is Data Collection? Everything a Beginner Needs to Know
Intelligent #AI/ #ML models are everywhere, be it, Predictive healthcare models, proactive diagnosis,
What is Data Labeling? Everything a Beginner Needs to Know
Download Infographics Intelligent AI models need to be trained extensively for being able to identify patterns, objects, and eventually make