In today’s AI landscape, quality data is a determining factor in the success of AI models. While most traditional datasets are scraped from the internet, patched together from outdated sources, or repurposed from legacy systems, Grably takes a radically different approach: we collect real-world, user-contributed data—and that makes all the difference.

Traditional data collection methods often suffer from poor conversion rates—only 1% to 8% of the raw inputs typically make it through to become usable, clean data. At Grably, our average approval rate is 52%, thanks to clearly defined tasks, real-time guidance, and an engaged contributor base. That means we deliver 6 to 50x more usable data from the same input volume—faster, cleaner, and without the expensive clean-up cycles traditional pipelines require.

Real, Human-Centered Data at the Core


Unlike generic datasets scraped from the web, Grably’s data is sourced directly from verified users, who contribute photos, videos, audio, or text inputs through our streamlined interface. This isn’t passive collection—it’s active, consented participation. Our users know what they’re contributing and why, which leads to cleaner, more contextual, and ethically sourced data.

Whether it’s a photo of a street corner in São Paulo, a recording of a conversation in Japanese, home surveillance video recording or a selfie taken with and without jewelry, we’re able to gather ultra-granular, task-specific data that would be nearly impossible to find at scale using conventional methods.

Granularity That Scales Across Any Industry


One of Grably’s biggest advantages is the granularity and specificity of our data. Because tasks are designed down to the detail—“a palm with jewelry in natural light” or “a picture of a crosswalk taken at dusk”—we can source exactly what model developers need. This makes our platform uniquely valuable for a wide range of industries:

  • Biometrics: Aging-resistant face recognition using ethically sourced photos spanning 10+ years.
  • Anti-Fraud: Forgery detection trained on real vs. fake IDs, documents, and attack samples.
  • Generative AI: Multi-modal training data with real conversations across text, voice, and media.
  • Real World: Hyper-specific image and video data like defected railways or unique dog behaviors.

This is data you simply can’t scrape or simulate with synthetic tools.

Every Dataset Is Verified and Moderated


Data collection at Grably doesn’t stop when a file is submitted. Each submission passes through three layers of moderation: on-device AI validation, server-side AI moderation, and finally, human-in-the-loop review. This multi-step process ensures that every data point matches task requirements for quality, relevance, context, and clarity—dramatically reducing noise and inconsistency, which are common flaws in traditional datasets.

Our team also works closely with partners to continually refine task prompts, ensuring each dataset is dialed in for the use case it’s meant to serve.

Full Annotation and Rich Metadata


High-quality inputs are just the beginning. Grably datasets come with detailed annotation and labeling. That means every dataset can be segmented by variables like lighting, object type, angle, emotion, user demographics or language—without expensive post-processing or guesswork.

This makes our data not only higher in quality but also faster and easier to train models with.

A New Standard in Data Collection


Traditional data pipelines are outdated. They rely on uncontrolled scraping, questionable sources, and endless cleaning cycles. Grably flips that model upside down—with user-sourced, verified, and richly labeled datasets, ready to power the next generation of AI.

From healthcare to mobility, fintech to retail, the future of machine learning doesn’t just need more data—it needs better data.

That’s what we’re building at Grably.