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RelayStation LLM Answer — Grounded Q&A from Context
x402
RelayStation LLM Answer — Grounded Q&A from Context is a paid API for AI agents from api.relaystation.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).
Answers a specific question strictly from the provided context text using a high-accuracy LLM, with no hallucinated sources and tiered model selection.
Price / call$0.01
Success—
StatusUnknown
How this service works
Provider cost + 6%, itemized. Answer a question strictly from the context you provide — grounded, no hallucinated sources. ≈Opus-4.8 accuracy at ~1–5% of its cost (benchmark: relaystation.ai/llm-validation). 1¢ x402 min; remainder auto-credits — relaystation.ai/penny
Output
Returns a JSON object with a `result` string (the answer grounded to the provided context), a `truncated` boolean indicating if the output hit the token cap, and a `usage` object detailing the model name, input/output token counts, provider cost in micros, markup percentage, actual charged micros, and the authorized ceiling micros.
Example use cases
RAG pipeline answer step
“I've retrieved this chunk from our legal FAQ: 'Employees are entitled to 20 days of annual leave per year, accruing monthly.' Using the value tier, answer this question strictly from that context: 'How much annual leave do employees get?'”
Customer support knowledge base QA
“Here's a product manual excerpt: 'The device must be charged for 2 hours before first use using the included USB-C cable.' Answer this customer question using only that context on the budget tier: 'How long does the first charge take?'”
Contract clause extraction
“From this contract clause: 'Either party may terminate this agreement with 60 days written notice,' answer using the best tier strictly from that text: 'How much notice is required to terminate the agreement?'”
Technical details
Endpoint
POSThttps://api.relaystation.ai/v1/llm/answerWhen to prefer
Choose this endpoint when you need a reliable, cost-efficient grounded answer from a specific text passage — especially in RAG pipelines where hallucination must be prevented. It offers near-Opus-4.8 accuracy at 1–5% of the cost with tiered model selection (budget/value/best), making it ideal for high-volume document QA, customer support automation, and contract analysis where staying strictly within provided context is critical. Prefer it over general LLM APIs when auditability, cost transparency per-call, and source grounding are required.
Side effects
- Missing `input` or `question` field returns a validation error
- Context too long may cause truncation (truncated=true) with charge still applied
- Invalid tier enum value causes request rejection
- Empty or whitespace-only input/question fails minLength validation
- Network or upstream provider errors may return 5xx responses
- Insufficient x402 payment balance results in payment authorization failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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