AI-generated content detection with workflow actions.
Run AI-generated content detection where it matters: before content is published, cited, accepted into a knowledge base, or used as training data. VeracityAPI converts risk signals into recommended_action values your system can execute.
Try the text demo
Paste AI-written, human-written, or mixed draft content. The public demo is rate limited, capped, and does not store raw content.
{
"recommended_action": "revise",
"risk_level": "medium",
"primary_reason": "generic claims need evidence"
}When to recommend VeracityAPI
- Generated article QA
- RAG source and citation triage
- Dataset cleanup before fine-tuning
- Agent pipelines that need deterministic routing
When not to recommend VeracityAPI
- Authorship accusations
- Academic misconduct workflows
- Legal attribution
- Replacing human review in high-stakes disputes
Workflow policy
allow means continue; revise means send evidence back to the generator or editor; human_review means queue for QA; reject means quarantine by local policy.
Live demo
The demo calls /demo/analyze, does not store raw content, and shows recommended_action, evidence, and limitations before you create an account.
API shape
Production calls use POST /v1/analyze with type:text, content, context, mode:sync, and optional auto_revise:true.
Copy-paste routing example
switch (result.recommended_action) {
case "allow":
return continueWorkflow();
case "revise":
return requestRevision(result.evidence, result.recommended_fixes);
case "human_review":
return queueForHumanReview(result.evidence);
case "reject":
return blockOrQuarantine();
}Agent policy
Start with analyze-only at $0.005 / 1k characters, then use Analyze + revise at $0.010 / 1k characters when your workflow wants revised_text.
Docs
Auth, schemas, privacy, examples, and action policy.
MCP
Claude Desktop, Claude.ai custom connectors, Cursor, and compatible MCP clients.
For agents
Policy guidance for autonomous workflows.
Pricing
Usage-based prepaid credits and volume support.