{"uid":"cap_GgjUGkNeWvgjZKpRnZxnP","slug":"verity-suite-ground-quick-calibrated-q-a-548559aa","name":"Verity Suite – Ground Quick (Calibrated Q&A)","description":"The trust fabric for AI agents — calibrated, fail-closed services agents pay per call.","url":"https://suite.veritylayer.dev/ground/quick","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"context":{"anyOf":[{"type":"string","maxLength":2000},{"type":"null"}],"title":"Context","default":null,"description":"optional context to consider"},"question":{"type":"string","title":"Question","maxLength":2000,"minLength":1,"description":"the question to answer"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.03","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.03/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.03","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.03","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_X2P0SGQfzhqsbiRI1fdel","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.03","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Answers a question with a calibrated confidence score, cited sources, caveats, and an optional Ed25519-signed receipt for auditability","exampleAgentPrompt":"Can you ask the Verity Suite grounding service: 'Was the Affordable Care Act signed into law in 2010?' — I need a calibrated confidence score, any relevant caveats, and the sources it actually used.","exampleUseCases":[{"title":"Fact-check claim before publishing","prompt":"Before we publish this article, ground-check this claim for me: 'The Eiffel Tower is taller than the Statue of Liberty including its pedestal.' Give me a confidence score and any caveats or sources."},{"title":"Agent self-verification in pipeline","prompt":"My agent just stated that 'lithium-ion batteries were first commercialized by Sony in 1991' — can you run that through the grounding service and tell me how confident it is and whether there are any gaps or uncertainties I should know about?"},{"title":"Research assistant due diligence","prompt":"I'm writing a report and need to verify this: 'The global EV market surpassed 10 million annual sales for the first time in 2022.' Give me a calibrated probability it's correct, the sources used, and flag any assumptions."}],"resultDescription":"Returns a structured object containing: a plain-language answer (or honest statement that the answer cannot be determined), a calibrated confidence score between 0 and 1, an array of actual sources used, an array of caveats (gaps, assumptions, uncertainties), and an optional Ed25519-signed VerityLayer receipt for independent verification.","failureModes":["Missing required 'question' field returns a validation error","Question exceeds 2000 character limit causes rejection","Context exceeds 2000 character limit causes rejection","Insufficient confidence in answer yields a low score with explicit 'cannot be determined' answer rather than a hallucinated response","Receipt is null if signing is not configured on the server side","Payment failure (HTTP 402) if USDC micropayment is not correctly attached"],"whenToPreferThis":"Choose this endpoint when your agent needs a calibrated, auditable answer rather than a raw LLM response — especially when accuracy confidence matters, when you need cited sources, when downstream decisions depend on knowing uncertainty, or when you require a cryptographically signed receipt for auditability. Prefer it over generic LLM calls when fail-closed behavior (honest 'I don't know') is more valuable than confident-sounding but potentially hallucinated output.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:42:29.660Z","isFirstParty":false}