VickyProlific has built a strong reputation as a platform that connects researchers and AI teams with high-quality human participants. Known for its vetted contributors and transparent approach, it has become a go-to choice for gathering feedback and running academic-style studies.
At the same time, the world of AI and data projects is expanding rapidly. Organisations now need not only human feedback, but also annotation, labelling, and multimodal datasets to train advanced AI models.
In this blog, we’ll look at some of the best options available today, from managed annotation services to enterprise-scale data providers.
1. Twine AI
Twine AI is purpose-built for AI training data projects. Twine provides end-to-end data collection, annotation and labelling across text, speech, image, and video.
With a curated global contributor network, Twine ensures data is diverse and bias-resistant. Every project is managed directly, with built-in quality assurance and compliance frameworks like GDPR and CCPA.
Twine is especially strong in:
- Video and speech datasets (accents, dialects, emotions, noisy environments)
- Image and video annotation for computer vision applications
- Multilingual NLP projects needing text/sentiment labelling
- Domain-specific data (e.g., healthcare, finance, retail)
For teams needing scalable, high-quality labelled data, Twine AI is one of the most complete Prolific alternatives.
2. LXT
Following its acquisition of Clickworker, LXT now connects enterprises with a crowd of over six million contributors worldwide. This makes it particularly useful for large-scale multilingual data collection and annotation.
If your project requires thousands of contributors across dozens of languages, LXT + Clickworker is a powerful option, with enterprise security standards that make it a trusted partner for global companies.
3. Defined.ai
Defined.ai blends a data marketplace with custom collection services. Companies can purchase ready-made datasets, such as speech corpora or text corpora, or commission tailored annotation projects.
Its strength lies in conversational AI and NLP, making it a great fit if you’re building chatbots, virtual assistants, or multilingual dialogue systems.
4. Labelbox
Labelbox isn’t a crowd platform; it’s an annotation infrastructure tool that helps enterprises manage their own data pipelines.
With features like automation-assisted labelling, workflow collaboration, and API integrations, Labelbox is best for companies that want to own their data operations in-house while still being able to connect with external annotation teams when needed.
5. SuperAnnotate
For projects that are heavily visual, SuperAnnotate is one of the best alternatives. It provides a collaboration-first platform for image and video annotation, with workflow tools that allow distributed teams to manage complex computer vision projects.
It’s particularly well-suited to industries like autonomous driving, medical imaging, and retail AI, where accuracy and efficiency in visual datasets are critical.
6. iMerit
iMerit combines managed annotation teams with domain-specific training. It’s known for serving industries such as medical AI, geospatial analysis, financial services, and agriculture.
For enterprises that require not only data volume but also specialist knowledge from their annotators, iMerit is a valuable option.
7. Sama
Sama has built its reputation on ethical sourcing and fair treatment of workers. It provides high-quality annotation for both computer vision and NLP projects, making it a reliable partner for companies that want to align data operations with their ESG commitments.
8. Surge AI
Surge AI is focused on the large language model (LLM) space, offering instruction-tuning, reinforcement learning from human feedback (RLHF), and evaluation sets.
If you’re working on generative AI or frontier models, Surge’s curated expert labelers provide the nuanced feedback that crowdsourcing platforms like Prolific can’t deliver.
9. Prolific Alternatives for Human Feedback
If your use case still leans more towards human feedback, surveys, or behavioural data, there are a few lighter alternatives worth mentioning:
- CloudResearch/Connect – A researcher-focused panel with detailed participant filters.
- Testable Minds – Designed for behavioural science and cognitive research.
- Respondent & User Interviews – Best for qualitative and UX-focused studies.
These platforms keep closer to Prolific’s roots, but don’t replace annotation and labelling services for AI projects.
Conclusion
Prolific continues to be a trusted platform for researchers and AI teams who need access to high-quality human participants. But as data needs grow more diverse, many organisations are expanding their toolkit by working with alternative providers.
The key is to match the right partner to your specific project needs, whether that’s collecting nuanced human feedback, scaling annotation across languages, or building industry-specific datasets. With more options than ever, teams can combine Prolific with other providers to create the complete data pipeline their AI projects require.
Raksha
