{"uid":"cap_iTv7vs98Xy-oK1UAWTrUq","slug":"foundrynet-inference-dd703b00","name":"FoundryNet Inference","description":"LLM inference proxy + data-enriched analysis + predictive intelligence. 17 data sources. MINT-attested outputs. x402-gated (Solana/Base USDC); an fnet_ Forge key bypasses.","url":"https://foundrynet-inference-production.up.railway.app/v1/infer","method":"POST","headers":{},"bodySchema":{"type":"object","required":["telemetry"],"properties":{"oem":{"type":"string","description":"Equipment manufacturer (optional; enables normalization)"},"telemetry":{"type":"object","description":"Key-value sensor readings; values may be scalars or numeric arrays (a 12+ point array enables forecasting)"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.1","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":"$0.1/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_bJvIRAq9_qyGIDBTmYwJ1","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs LLM-powered analysis and predictive intelligence on equipment telemetry data, enriched from 17 data sources, with MINT-attested outputs, gated via x402 micropayment (Solana/Base USDC).","exampleAgentPrompt":"Analyze this compressor telemetry from a Siemens unit — temperature: [72, 74, 78, 83, 89, 95, 98, 102, 107, 112, 118, 125], vibration: [0.12, 0.13, 0.15, 0.18], pressure: 14.7 — and give me an AI-enriched forecast and anomaly report with attested results.","exampleUseCases":null,"resultDescription":"Returns LLM-generated analysis of the submitted telemetry, enriched with data from 17 external sources. If 12+ time-series data points are provided, includes a predictive forecast. Output is MINT-attested for verifiability. Typical fields include anomaly flags, trend analysis, risk scoring, and forward projections.","failureModes":["402 Payment Required — no valid x402 payment or fnet_ Forge key provided","400 Bad Request — missing required 'telemetry' field or malformed sensor values","422 Unprocessable Entity — telemetry values are non-numeric or improperly structured","503 Service Unavailable — upstream data source enrichment or LLM inference failure","Forecasting disabled — fewer than 12 time-series data points provided, only base analysis returned"],"whenToPreferThis":"Use this endpoint when you need LLM-powered, multi-source-enriched analysis of equipment sensor telemetry — especially when MINT attestation of the output is required for auditability, or when predictive forecasting from time-series sensor arrays is needed. Prefer over generic LLM APIs when industrial equipment context (OEM normalization) and 17-source enrichment add meaningful signal.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:34:38.772Z","isFirstParty":false}