Provide ethical, high-quality data labeling for AI model training
Innovatiana helps companies prepare datasets for their AI models by using trained data labelers and domain experts. This ensures your data is accurate, unbiased, and ready for training, saving you time and resources. They focus on ethical practices and security, so you can trust your data is handled responsibly.
Recruit and train specialized data labelers for your project needs
Reject crowdsourcing for full traceability and responsible data handling
Dedicated manager oversees projects for streamlined production and quality
Pay per task or dataset with no hidden costs or subscriptions
Protect data with compliance to GDPR, ISO, and AI ethics standards
Through rigorous methodology, systematic quality controls, and domain experts to deliver reliable datasets.
Per task or per dataset delivered, with no subscriptions, set-up fees, or hidden costs.
Yes, we integrate with platforms like Label Studio, CVAT, or your own tools, or use our secure environments.
We handle images, videos, text, documents, and multimodal data for computer vision, NLP, Gen-AI, and more.
This tool is best for:
Explore more niche AI tool websites by clicking on a tag* (works only if it has enough tools).
Innovatiana can be integrated with these apps and services:
Recent data shows that Innovatiana has 43.2K monthly visits (-0.2% decrease from the previous month), 45.0% bounce rate, and average 1.60 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 5 countries worldwide, led by France contributing 15% of total traffic. SEO performance is shown by 5 tracked keywords, with "exemple de dataset" being the top-performing keyword with 280 monthly searches. See below for more info.
Monthly Visits
43.2K
Pages per Visit
1.60
Bounce Rate
45.0%
Average Time on Site
31s
| SEO Keyword | Volume | CPC |
|---|---|---|
| exemple de dataset | 280 | - |
| gráficas de conjuntos de datos donde no se puede utilizar la regresión lineal simple | 300 | - |
| kl divergence | 21.8K | - |
| publicly available datasets for crop health monitoring | 430 | - |
| text data to classify | 150 | - |
| Source | Traffic Share |
|---|---|
| Direct | 32% |
| Search | 52% |
| Social | 4% |
| Referrals | 11% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 15% |
 | 7% |
 | 6% |
 | 6% |
 | 5% |
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