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 annotation provider with a mixed and polarized review profile spanning enterprise clients and individual contributors. While one anchor Clutch review demonstrates measurable outcomes in medical AI, the broader review pool is heavily fragmented between enthusiastic short-form endorsements and serious, recurring allegations of non-payment and fraud from vendors and workers. The pattern of payment complaints across multiple years and geographies constitutes a meaningful reliability concern that tempers otherwise positive signals.
Performance breakdown
Technical expertise
MixedThe sole high-density Clutch review confirms strong AI data annotation capabilities for EHR and clinical NLP use cases. However, a detailed negative account describes a medical imaging project where Shaip delivered severely incomplete metadata and recycled publicly available images, raising data quality concerns.
Project management & delivery
MixedThe Clutch review cites a 4–6 week reduction in data preparation time and consistent milestone communication, reflecting sound delivery processes. Countering this, one detailed negative review documents a four-month engagement with persistent requirement mismatches and unresolved project outcomes, indicating inconsistent delivery standards.
Communication & collaboration
MixedMultiple contributor-side reviews praise responsive support staff and clear project guidelines on Shaip's tasking platform. However, several independent reviewers report being ignored after project completion when pursuing payment resolution, suggesting communication quality may differ significantly between client-facing and vendor-facing interactions.
Reliability
WeakA recurring and multi-year pattern of non-payment allegations from vendors, contributors, and subcontractors across multiple geographies constitutes a significant reliability risk. Claims span from 2022 to 2025 and include withheld compensation ranging from individual contributors to amounts equivalent to approximately 5 lakh INR, indicating a systemic rather than isolated issue.
Client satisfaction & outcomes
MixedThe single verifiable enterprise client review reports a 20–25% reduction in internal validation effort and tangible time savings on data preparation, representing a strong business outcome. Satisfaction among contributor-side users is split, with positive platform experiences offset by serious fraud allegations and unresolved compensation disputes.
Best for
Shaip may be best suited for enterprise or mid-market AI teams requiring data annotation and AI training datasets, particularly in healthcare. Clients seeking high-volume, managed data pipelines should conduct thorough vendor due diligence given documented payment and delivery concerns.
Clients info
The most clearly documented enterprise engagement is in the medical and healthcare sector, specifically clinical NLP and EHR model development. Client sizes at the enterprise level appear to be small-to-mid-market firms (11–50 employees based on available data). The single confirmed project budget falls in the $10,000–$49,999 range; broader budget data is not available. Primary industries represented include Medical / Healthcare, AI / Technology. Typical client size bands include 11–50 Employees. Common project budget ranges include $10,000 to $49,999.
Review strength
31 reviews were analyzed across three platforms, spanning from June 2022 to early 2026. The evidence base is broad in volume but narrow in enterprise-grade depth, with only one high-density review containing specific metrics. The majority of reviews are from individual contributors rather than enterprise clients, limiting the applicability of much of the data to a B2B procurement context. A notable portion of reviews are more than one year old. Review date range: 2022-06-28 - 2026-08-06.
Performance breakdown
Technical expertise
MixedThe sole high-density Clutch review confirms strong AI data annotation capabilities for EHR and clinical NLP use cases. However, a detailed negative account describes a medical imaging project where Shaip delivered severely incomplete metadata and recycled publicly available images, raising data quality concerns.
Project management & delivery
MixedThe Clutch review cites a 4–6 week reduction in data preparation time and consistent milestone communication, reflecting sound delivery processes. Countering this, one detailed negative review documents a four-month engagement with persistent requirement mismatches and unresolved project outcomes, indicating inconsistent delivery standards.
Communication & collaboration
MixedMultiple contributor-side reviews praise responsive support staff and clear project guidelines on Shaip's tasking platform. However, several independent reviewers report being ignored after project completion when pursuing payment resolution, suggesting communication quality may differ significantly between client-facing and vendor-facing interactions.
Reliability
WeakA recurring and multi-year pattern of non-payment allegations from vendors, contributors, and subcontractors across multiple geographies constitutes a significant reliability risk. Claims span from 2022 to 2025 and include withheld compensation ranging from individual contributors to amounts equivalent to approximately 5 lakh INR, indicating a systemic rather than isolated issue.
Client satisfaction & outcomes
MixedThe single verifiable enterprise client review reports a 20–25% reduction in internal validation effort and tangible time savings on data preparation, representing a strong business outcome. Satisfaction among contributor-side users is split, with positive platform experiences offset by serious fraud allegations and unresolved compensation disputes.