Shaip provides high-quality data across multiple data types (text, audio, image & video) to companies looking to build unbiased and high quality AI/ML models. Shaip licenses, collects and annotates data for Healthcare, Conversational AI, Computer Vision and Generative AI/LLM use cases. Going beyond data, Shaip offers a complete Responsible LLM Toolkit to align, evaluate, and enhance large language models using reinforcement learning from human feedback (RLHF). Headquartered in Kentucky with offices in Silicon Valley and India, our global team blends data science expertise with deep industry knowledge.
Shaip provides high-quality data across multiple data types (text, audio, image & video) to companies looking to build unbiased and high quality AI/ML models. Shaip licenses, collects and annotates data for Healthcare, Conversational AI, Computer Vision and Generative AI/LLM use cases. Going beyond data, Shaip offers a complete Responsible LLM Toolkit to align, evaluate, and enhance large language models using reinforcement learning from human feedback (RLHF). Headquartered in Kentucky with offices in Silicon Valley and India, our global team blends data science expertise with deep industry knowledge.
AI Overview
Performance snapshot
Shaip is an AI data services and data annotation vendor with a deeply polarized review profile. Positive reviews predominantly reflect satisfactory experiences from individual contributors and vendors praising platform usability and timely payments, while a persistent and recurring thread of negative reviews specifically cites non-payment, fraudulent conduct, and unethical practices spanning from 2022 to early 2026. The volume and consistency of payment-related complaints across multiple years constitute a significant concern for prospective clients. No anchor reviews containing specific metrics, budgets, or quantified outcomes were identified in the dataset.
Performance breakdown
Technical expertise
MixedSome reviewers commend Shaip's data annotation accuracy and AI data services quality. However, one detailed negative review describes fundamentally flawed sample data delivery for a medical imaging project, with incomplete metadata and substandard images, raising material quality-control concerns.
Project management & delivery
MixedSeveral reviewers cite clear project guidelines and a systematic dashboard process. Conversely, one client reports four months of misaligned requirements and a failed deliverable, and multiple contributors report projects completed without any follow-through on compensation or resolution.
Communication & collaboration
MixedMultiple reviewers note responsive support teams and direct contact with project managers. However, several negative reviewers report being unable to reach anyone after project completion, with repeated outreach attempts going unanswered, indicating inconsistent availability.
Reliability
WeakA recurring and multi-year pattern of non-payment complaints from vendors and contributors — spanning 2022 through January 2026 — significantly undermines reliability. The consistency of this issue across geographies and time periods represents a material and documented risk pattern.
Client satisfaction & outcomes
MixedPositive sentiment exists among contributors and some enterprise clients regarding data quality and project outcomes. However, no quantified business results or ROI metrics are present, and unresolved payment disputes and at least one failed enterprise project limit confidence in consistent value delivery.
Best for
Shaip may be considered for AI training data collection, data annotation, and data labeling tasks by organizations that can independently verify payment terms and contractual protections. Best suited for AI/ML teams requiring labeled datasets at scale.
Clients info
Reviewers represent a mix of individual data contributors, freelance vendors, and at least one enterprise-level AI medical imaging client. Client geographies span India, the US, UK, Kenya, Nigeria, and Germany. Project budget references are limited; one enterprise project referenced a pricing model of approximately $10 per image for medical imaging data. Most contributors do not disclose organizational size or formal budget ranges. Primary industries represented include Artificial Intelligence / Machine Learning, Healthcare / Medical Imaging, Data Services. Typical client size bands include Individual contributors / Freelancers, Small vendors, Mid-market enterprise (inferred from one medical imaging client). Common project budget ranges include Approximately $10/image for medical imaging annotation (single reference), Not enough data for broader range.
Review strength
The assessment is based on 30 reviews across 2 platforms, spanning from June 2022 to January 2026. The majority of reviews are recent, though a notable portion of the negative reviews date back to 2022 and 2023, indicating a long-standing pattern rather than isolated incidents. Many positive reviews are brief and lack substantive detail, reducing their analytical weight. Review date range: 2022-06-28 - 2026-01-15.
Performance breakdown
Technical expertise
MixedSome reviewers commend Shaip's data annotation accuracy and AI data services quality. However, one detailed negative review describes fundamentally flawed sample data delivery for a medical imaging project, with incomplete metadata and substandard images, raising material quality-control concerns.
Project management & delivery
MixedSeveral reviewers cite clear project guidelines and a systematic dashboard process. Conversely, one client reports four months of misaligned requirements and a failed deliverable, and multiple contributors report projects completed without any follow-through on compensation or resolution.
Communication & collaboration
MixedMultiple reviewers note responsive support teams and direct contact with project managers. However, several negative reviewers report being unable to reach anyone after project completion, with repeated outreach attempts going unanswered, indicating inconsistent availability.
Reliability
WeakA recurring and multi-year pattern of non-payment complaints from vendors and contributors — spanning 2022 through January 2026 — significantly undermines reliability. The consistency of this issue across geographies and time periods represents a material and documented risk pattern.
Client satisfaction & outcomes
MixedPositive sentiment exists among contributors and some enterprise clients regarding data quality and project outcomes. However, no quantified business results or ROI metrics are present, and unresolved payment disputes and at least one failed enterprise project limit confidence in consistent value delivery.