Interactive AI detection demo
The demo page routes commercial and educational visitors into a scan workflow, sample report, or API evaluation depending on intent.
Report example
Demo scan
- Upload sample
- Inspect result
- Review evidence
- Export report
Search intent
Interactive detector demo conversion
Primary evidence
Sample reports, Confidence score, Forensic indicators
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
Sample uploads
Visitors should be able to test text, image, audio, video, and code examples without reading a landing page first.
- Text sample
- Image sample
- Voice sample
- Video sample
Report preview
Every demo result should show a confidence band, evidence indicators, metadata status, and a next-step recommendation.
- Confidence score
- Evidence cards
- Limitations
- CTA by intent
Conversion paths
Detector pages should lead to scan, research pages to report downloads, enterprise pages to demos, and API pages to keys.
- Try scan
- Download report
- Book demo
- Get API key
Use cases
Detector page CTA
Enterprise demo CTA
Research asset preview
Sample report preview
Media preview
Safe sample, redacted upload, or generated demonstration asset.
Public reports should only expose media that is lawful, consented, and safe to publish.
Confidence
Ready
Upload sample
Evidence item linked to score calibration, source context, and known uncertainty.
Inspect result
Evidence item linked to score calibration, source context, and known uncertainty.
Review evidence
Evidence item linked to score calibration, source context, and known uncertainty.
Export report
Evidence item linked to score calibration, source context, and known uncertainty.
Evaluation table
| Criterion | What to check | Why it matters |
|---|---|---|
| Coverage | Text, image, audio, video, code. | Synthetic media risk rarely stays in one format. |
| Explainability | Score, indicators, timestamps, metadata, limitations. | Reviewers need evidence, not a black-box verdict. |
| Accuracy risk | False positives, false negatives, calibration. | High-impact workflows require documented uncertainty. |
| Workflow fit | API, batch, reports, retention, reviewer queues. | Search traffic must convert into a usable product path. |
Methodology and limitations
How to read the score
Detection output should be read as calibrated evidence. A high score means the observed signals are consistent with synthetic or manipulated media under the current model and sample conditions. It does not prove authorship, intent, or model attribution by itself.
Where review is required
Short samples, heavy editing, compression, translation, re-recording, mixed human-AI content, and unseen generators can reduce confidence. Use human review, source context, and policy thresholds before high-impact enforcement.
Next step
Match the action to the visitor intent: detector pages should lead to a scan, research pages to a downloadable report, enterprise pages to a demo, and developer pages to API keys or playground examples.
FAQ
Is this the production scanner?
This page is the public conversion layer. Production scanning is handled by the ZeroTrue app.
Which modalities should the demo support?
Text, image, audio, video, code, and report examples should be represented.
What should a detector CTA do?
It should lead directly to a scan or sample upload, not a generic contact form.