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Product Roadmap

What We Are Building

Aiscern is an early-access product built by a solo founder. This page is an honest record of what works, what is coming, and where we fall short.

Current Status

Text AI detection using 3-model RoBERTa ensemble (~85% accuracy)
Image detection using ViT + CLIP ensemble (~82% accuracy)
Audio deepfake detection via wav2vec2 (~79% accuracy)
Video frame-level deepfake analysis (~76% accuracy — experimental)
Batch scanning up to 20 files simultaneously
Shareable scan result links
ARIA AI detection assistant (chat)
Free tier with 10 scans/day — no account required

Next 3 Months

Improve audio detection accuracy on WaveFake benchmarks
Launch PDF report export for Pro users
Release public REST API for Team plans
Video temporal consistency improvements
Stripe billing integration for Pro and Team plans
Public leaderboard with per-model accuracy breakdown

Known Limitations

Video detection is experimental

~76% accuracy. Short clips and low-resolution video are less reliable. Do not use for high-stakes decisions alone.

Short audio clips are unreliable

Clips under 5 seconds do not give the model enough signal. Results on very short audio should be treated as indicative only.

Non-native English may trigger false positives

Text detection models were primarily trained on English. Non-native phrasing patterns can sometimes appear machine-like to the model.

Human-edited AI content is harder to detect

When AI output has been manually edited or paraphrased, detection accuracy drops. There is no perfect detector.

Results are probabilistic, not definitive

All verdicts come with a confidence score. Use human judgment alongside Aiscern results for any consequential decision.

Last updated: April 2026 · Built by Anas Ali in Islamabad, Pakistan.

See accuracy methodology