Annotation, evaluation and validation for foundation models, Physical AI and enterprise AI.
Annotation and quality checks run in parallel, so you train on data you can trust, sooner.
Before. Not every video is worth annotating. We flag repetitive footage before you pay to label it.
During. An annotation model built on your scenarios, checked by our contributor network as it runs. Every episode described at task, sub-task and atomic-motion level.
After. You see what holds up, what doesn’t, and where more data would help.
Structure the data your models learn from. Text, video, audio and images.
Check whether model outputs are right, even when the rubric doesn’t exist yet.
Make sure data, labels and outputs hold up before you rely on them.
Some decisions a model can make. Some need people. Some need an expert. We break the work into decisions and route each one by confidence.
When a model can decide with confidence. Fast and automated.
When it takes human perception, or more volume than any team can cover. Representative judgment at statistical significance.
When it’s specialized or ambiguous. Verified domain experts decide.
Layers can run in parallel. Every correction feeds back into the system.
An ad, a translation, a reply to a customer: what matters is how people read it. Crowd as a Judge measures that at statistical significance, with a 100M+ contributor network carrying the volume.
Built for: advertising · commerce · translation · customer care
Physical AI, defense, social and commerce.
Annotated across three dimensions: executed vs. prompt, execution quality and success. No benchmark existed; the methodology was co-built with the research team.
The agency’s cleared experts couldn’t keep up alone. Our network handled most of the recognition on declassified aerial thermal video; only judgment-grade calls went in-house.
5M+ objects identified · Operational-grade accuracy · Ongoing
Live validation of production models. A global network handles the volume; ambiguous cases go to domain experts.
Live validation of the models behind search relevance and ad quality. Ambiguous cases go to domain experts.
Tasq was formed from the merger of Tasq.ai, the AI orchestration platform built for edge-case decisions, and BLEND, the world’s largest network of verified domain experts across 120 languages.
Annotation, evaluation and validation on the same judgment architecture.
No strategic investors from our client base. Our verdicts stay neutral.
100M+ contributors. 75K+ verified experts. 120 languages.
Tasq.ai is the data processing and management layer for AI, enabling data teams to optimize model training, evaluation and validation across text, video, audio and images.
It turns large volumes of robot, human and teleoperation video into structured training data, using a customer-specific annotation model and parallel human validation.
Representative human perception and preference, measured at statistical significance, with a 100M+ contributor network carrying the volume. It’s for outputs only people can judge.
Annotation, evaluation and validation run on one judgment architecture. Everyone else makes you pick a tier up front. We decide per question: a model, the contributor network or a domain expert. Tasq.ai is not a marketplace or a self-serve tool.
No. For Physical AI, Tasq.ai builds its own annotation model per customer. It’s a tool for producing your annotations, not your model.
Tell us what your models decide. We’ll show you where judgment matters.
Capital, channel, M&A. We’re building the independent trust layer for production AI, and scaling fast.