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SYNTHORA Research Pack
x402
SYNTHORA Research Pack is a paid API for AI agents from research-pack.hergertsynthora.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-19).
Synthesizes research evidence for a decision question, weighing source quality, contradictions, uncertainty, and practical implications using Qwen3-27B
Price / call$0.005
Success—
StatusUnknown
How this service works
Research synthesis for a decision: weigh source quality, contradictions, uncertainty and practical implications in the supplied evidence. Qwen3.8-27B; 0.005 USDC on Base.
Output
Returns a structured JSON object containing extracted facts, cited sources, a decision summary, freshness assessment of the evidence, a numeric confidence score (0-1), inferences drawn, known limitations, contradictions identified across sources, and a concrete recommended action based on the synthesized evidence.
Example use cases
Investment thesis evidence review
“I'm deciding whether to invest in solid-state battery startups — synthesize the current research on their commercial readiness, call out where experts disagree, tell me how fresh the evidence is, and give me a confidence score on the outlook.”
Medical treatment comparison
“I need to decide between two first-line treatments for type-2 diabetes management: metformin versus GLP-1 agonists. Can you weigh the clinical evidence, highlight any contradictory findings, and tell me what the research actually recommends?”
Policy decision research brief
“Our team is deciding whether to implement a four-day workweek — can you pull together the research evidence, flag limitations and contradictions in the studies, give me your confidence level, and recommend whether the evidence supports moving forward?”
Technical details
Endpoint
POSThttps://research-pack.hergertsynthora.com/service?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a structured, reasoned synthesis of evidence for a specific decision — not just a summary. It is especially valuable when you have conflicting sources, want an explicit confidence score, need contradictions surfaced, or require a concrete recommended action rather than a general overview. Prefer it over raw LLM prompting when you need the output in a consistent schema (facts, sources, inferences, limitations, confidence, recommendation) that a downstream agent or workflow can parse reliably.
Side effects
- Missing or vague question field returns an error or low-quality synthesis
- Insufficient context provided leads to low confidence score and broad limitations
- Very niche or obscure topic may yield few facts and hedged recommendations
- Contradictory evidence without clear resolution may return an inconclusive decision field
- Payment failure via x402 prevents execution entirely
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| question | string | Decision question with optional evidence/context |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"niche": "research_pack",
"result": {
"facts": [],
"sources": [],
"decision": "...",
"freshness": "...",
"confidence": 0,
"inferences": [],
"limitations": [],
"contradictions": [],
"recommended_action": "..."
}
}
}Tags
x402
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