When we started building AI Aware, most of the "AI detection" conversation was still focused on text (is this essay or article AI-generated, yes or no). But the more fraud cases and deepfake cases we looked at, it became clear text-only detection was would not be enough.
A convincing scam now combines a voice-cloned phone call, a manipulated image with AI-written follow-up text - three different AI generation methods combined for one attack. If an AI detector only checks the text, it is missing two-thirds of the picture. So we built AI Aware to handle text, deepfake video, images, and voice cloning in a single tool.
A few decisions we made along the way:
Confidence scores rather than just binary verdicts. A flat "AI or not" output is hard to get into. We wanted people to see why something got flagged - paragraph-level highlighting and confidence scores for text, specific facial and lighting artifacts for video, irregularities for images, spectral and prosody analysis for audio. We wanted explainability.
Ensemble over single-model. People actively try to "humanize" AI content to slip past detectors, so we run multiple models together so evasion attempts have to beat more than one approach.
Built for smaller teams, not just enterprise procurement. We run usage-based pricing with a free trial, starting at $15, so smaller teams and solo builders can actually test it against their own data before committing.
We've been working with organizations across education, media, HR, and legal- anywhere someone needs to verify whether what they're looking at is real. If it's useful for you, you can try AI Aware here.
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