Pricing for synthetic media detection
ZeroTrue pricing is organized around usage, modality coverage, API volume, and enterprise review requirements.
Report example
Plan fit assessment
- Multiple modalities
- Need for review logs
- Batch checks
- Enterprise threshold tuning
Search intent
Pricing evaluation
Primary evidence
Usage volume, Supported modalities, Retention requirements
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
What affects pricing
Detection costs vary by media size, modality, latency requirements, retention settings, and review volume.
- Text and code checks
- Image and document analysis
- Audio and video processing
- Enterprise API volume
API and review workflows
Teams can evaluate ZeroTrue as a web scanner, API layer, or enterprise review signal.
- Usage-based API plans
- Batch processing needs
- Reviewer evidence requirements
- Security and retention controls
Enterprise buying criteria
A fair evaluation should include accuracy, false positive handling, auditability, and integration effort.
- Run a representative sample set
- Measure review usefulness
- Confirm data handling needs
- Estimate monthly media volume
Use cases
Developer API rollout
Trust and safety review
Enterprise fraud investigation
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
Recommended: API evaluation
Multiple modalities
Evidence item linked to score calibration, source context, and known uncertainty.
Need for review logs
Evidence item linked to score calibration, source context, and known uncertainty.
Batch checks
Evidence item linked to score calibration, source context, and known uncertainty.
Enterprise threshold tuning
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 there an API plan?
Yes. ZeroTrue is designed for API access as well as web scanning.
Do video and audio cost the same as text?
Typically no. Larger media requires different processing and latency assumptions.
Can enterprise teams request custom terms?
Yes. Enterprise needs often include security, retention, volume, and support requirements.