# FoundryNet Inference

> FoundryNet Inference is a paid API for AI agents from foundrynet-inference-production.up.railway.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-14).

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).

## Facts

- Endpoint: POST https://foundrynet-inference-production.up.railway.app/v1/infer
- Price: $0.1/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/foundrynet-inference-dd703b00
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_iTv7vs98Xy-oK1UAWTrUq

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability foundrynet-inference-dd703b00 -d '<json body>'
```

Example prompt: 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.

## When to prefer this

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.

## Known failure modes

- 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

## How this service works

LLM inference proxy + data-enriched analysis + predictive intelligence. 17 data sources. MINT-attested outputs. x402-gated (Solana/Base USDC); an fnet_ Forge key bypasses.

## Output

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.

## Request schema (JSON Schema)

```json
{
 "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)"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/foundrynet-inference-dd703b00/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from foundrynet-inference-production.up.railway.app](https://www.zero.xyz/host/foundrynet-inference-production.up.railway.app/llms.txt)