PROCESS / BATCH · TELEMETRY NORMALIZATION
16,908 confirmed mappings·18 OEM families·14 protocols·96% field coverage
Forge turns raw Food & beverage process tags into one clean, governed schema your AI agent already understands. One MCP call, no per-line integration project.
F&B lines mix CIP state, flow, temperature and fill counts across PLCs from a dozen skid vendors, so line-level analytics stall on tag mapping before they ever start.
Typical Food & beverage process equipment communicates over OPC UA, Modbus TCP, MQTT Sparkplug B, BACnet/IP. Each firmware revision, model line and integrator names the same physical quantity differently, so every AI or analytics project pays the integration tax again, the tax that cancels 40% of industrial AI deployments before they ship.
Forge normalizes Food & beverage process telemetry into Forge's canonical field set, the same 366 canonical fields used across all 18 manufacturers. Point Forge at the line, pass oem: "food", and spindle speed is spindle_speed_rpm whether it came from a Food & beverage process controller or anything else on the floor.
Vertical pack · 23 curated mappings
Confirmed canonical fields your agent gets from Food & beverage process lines, drawn from Forge's 16,908-mapping corpus:
| Raw tag in | Canonical field out | Meaning | |
|---|---|---|---|
| CIP_STATE: "RINSE" | → | process.cip_state: "RINSE" | clean-in-place state |
| FLOW_LPM: 210 | → | process.flow_rate: 210 | product flow |
| TANK_LVL: 68 | → | process.level_pct: 68 | tank level % |
| PROD_TEMP: 4.2 | → | process.reactor.temp_c: 4.2 | product temperature |
No SDK, no per-tag mapping, no historian project. Connect the MCP server and normalize live Food & beverage process data:
Unknown tags never get force-mapped. Fields below the confidence floor abstain rather than guess, so your agent never acts on a bad reading.