← AI Data Annotation Services

Synthetic Influencer & AI-Avatar Trait Labeling

Brands adopting AI brand ambassadors and virtual agents are one uncanny-valley moment away from public backlash. We score the frame-by-frame realism that keeps that from happening.

The Niche

Annotating realism and consistency for synthetic media — before it reaches an audience.

As brands roll out AI avatars, virtual spokespeople, and synthetic ad talent, the models behind them need training data that flags exactly where they fall into the uncanny valley — a subjective judgment call no automated detector fully replaces.

We review generated video frame by frame and label the specific artifacts and inconsistencies that make synthetic media feel "off," giving the teams building or fine-tuning these models the labeled examples they need to fix it.

What We Annotate
Visual Artifacts
Distorted hands, hair, or edges, unnatural blinking, warping — the visible tells that break believability.
Unnatural Eye Contact
Gaze that lingers too long, drifts oddly, or never quite lands — one of the strongest uncanny-valley signals.
Audio-Video Sync Lag
Lip movement that trails or leads the audio track, even by a few frames.
Micro-Expression Errors
Emotional expressions that don't match tone, or transitions that feel mechanical rather than human.
Why It Sells

Brands know the reputational cost of a synthetic ambassador going viral for the wrong reasons. They pay well for a team that can meticulously catch the errors before launch — and this is squarely brand-judgment work, not a technical computer-vision task.

FAQ
Can't deepfake-detection software catch these issues automatically?
Detection tools flag statistical anomalies, but whether a specific micro-expression or gaze pattern feels "off" to a real viewer is a subjective, brand-context judgment — the same kind a creative director makes reviewing a cut of an ad.
What kind of synthetic media do you review?
AI brand ambassadors, virtual customer service agents, synthetic ad talent, and any generative video model output intended for a public-facing brand use.
Do you only flag problems, or fix them too?
We label and score the issues as structured feedback your model team uses to retrain or fine-tune — the annotation itself is what drives the fix.
Get Started

Let's scope your synthetic media QA.

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