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SYNTHORA Decision Engine
x402
SYNTHORA Decision Engine is a paid API for AI agents from decision-engine.hergertsynthora.com, paid per call via x402, $0.005/call, status down (last checked 2026-10-30).
Synthesizes a traceable decision recommendation from a question plus optional evidence, separating observed facts from inferences using Qwen3 reasoning.
Price / call$0.005
Success—
StatusDown
How this service works
General decision synthesis for an agent that has a question plus optional evidence or context. Separates observed facts from inferences and gives a traceable recommended action. Qwen3.8-27B; 0.005 USDC on Base.
Output
Returns a structured JSON object containing: a list of observed facts extracted from the question/context, a list of inferences drawn by the model, a decision summary, a recommended_action string, a numeric confidence score (0-1), data freshness assessment, known limitations of the analysis, and any contradictions detected in the provided evidence.
Example use cases
Medical triage decision support
“My patient has fever, elevated CRP, and a recent travel history to a malaria-endemic region but negative rapid tests. Help me decide whether to start empirical antimalarial treatment anyway — separate the facts from inferences and give me a recommended action with your confidence and limitations noted.”
Investment risk assessment
“I'm deciding whether to put $50k into a real estate deal in Phoenix — rental yields are 6%, the neighborhood is gentrifying, but interest rates just rose 50bps. Synthesize a decision for me, tell me what's observed fact vs inference, flag any contradictions, and give me a clear recommended action.”
Product launch go/no-go decision
“We have a product launch scheduled for next Friday — beta feedback scores are 4.2/5, but our payment integration has one unresolved bug and two enterprise clients haven't signed NDAs yet. Should we launch on time? Give me a traceable decision with facts, inferences, confidence level, and a recommended action.”
Technical details
Endpoint
POSThttps://decision-engine.hergertsynthora.com/service?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a structured, traceable decision output that explicitly separates facts from inferences — not just a free-text opinion. It's especially useful when the agent needs to audit its own reasoning, show confidence levels, or flag contradictions in the supplied evidence. Prefer it over a generic LLM chat call when the output must be machine-parseable (facts[], inferences[], recommended_action) and when decisions need to be logged or explained downstream.
Side effects
- Missing or empty question field returns an error response
- Ambiguous or very short questions may yield low confidence scores and broad limitations
- Model may fail to distinguish user-supplied facts from inferences if context is poorly structured
- Contradictions in the input may cause the model to hedge heavily without a clear recommended action
- Network or payment authorization failures return non-ok responses
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| question | string | Decision question with optional evidence/context |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"niche": "decision_engine",
"result": {
"facts": [],
"sources": [],
"decision": "...",
"freshness": "...",
"confidence": 0,
"inferences": [],
"limitations": [],
"contradictions": [],
"recommended_action": "..."
}
}
}Tags
x402
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