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TITANHOLD AI Quality Check
x402
TITANHOLD AI Quality Check is a paid API for AI agents from x402.titanhold.app, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-20).
Checks an AI-generated image or short video for common generation defects such as extra fingers, garbled text, wrong vehicle geometry, physics errors, and flicker, returning a pass/fail score and defect list.
Price / call$0.02
Success—
StatusUnknown
How this service works
Paid check of an AI-generated image or short video for typical generation defects (extra fingers, garbled text, wrong vehicle geometry, physics, flicker). Runs a local vision model; files are deleted after the check.
Output
Returns a JSON object with: whether the file passed or failed, the vision model used, a numeric score from 0-100, an array of defects each with type, detail, and severity, a human-readable reasons array, and the number of frames checked.
Example use cases
Pre-delivery check for AI art marketplace
“Before I send this AI portrait to the client, check it for defects like extra fingers or weird face geometry — the brief was 'photorealistic portrait of a woman in a red dress outdoors' — fail it if it scores under 80. Here's the URL: https://mystore.com/outputs/portrait_final.png”
Video frame defect detection for generative pipeline
“I have a short AI-generated video clip at https://renders.example.com/clip_001.mp4 — can you check it for flickering, physics errors, or geometry issues before I publish it? Flag anything major and tell me the overall score.”
Batch QA gate in automated content workflow
“We just generated an image from a prompt about 'a futuristic cityscape at night with flying cars' — here's the base64 data. Run the quality check and tell me if it passes or fails a score of 70, and list any defects you find.”
Technical details
Endpoint
POSThttps://x402.titanhold.app/v1/check/image?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need automated, low-cost ($0.02) quality gating for AI-generated images or short videos before delivery or publication — especially when checking for anatomy errors, text legibility, vehicle geometry, or physics plausibility. It is purpose-built for generative AI artifact detection rather than general image analysis, and files are deleted after inspection for privacy.
Side effects
- Invalid or inaccessible URL returns an error and no score
- Base64 data that is malformed or too large may be rejected
- Score threshold set too high causes legitimate content to fail
- Model may miss subtle defects in complex scenes
- Short videos may have insufficient frames for reliable flicker detection
- Payment failure (x402) blocks the check from running
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| url | string | https URL of the delivered file. Alternative: data (base64). |
| data | string | Base64 file bytes. Alternative to url. |
| brief | string | What was ordered, used to judge the file against the brief. Max 500 chars. |
| minScore | number | Fail below this 0-100 score. Default 70. |
Output
Schema · Response
{
"type": "json",
"example": {
"kind": "image",
"pass": false,
"model": "ollama:qwen2.5vl:7b",
"score": 55,
"defects": [
{
"type": "vehicle_geometry",
"detail": "car has three doors",
"severity": "major"
}
],
"reasons": [
"major vehicle_geometry: car has three doors"
],
"framesChecked": 1
}
}Tags
x402
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