HEAVY VEHICLE / OFF-HIGHWAY · CANONICAL DATA MODEL

Freightliner canonical data model: connect your Freightliner equipment to any AI agent

2,461 confirmed mappings·12 protocols·91% field coverage

Forge turns raw Freightliner tags into one clean, governed schema your AI agent already understands. One MCP call, no per-vehicle integration project.

Try it free: 3 machines, no credit card → See it work →

Paste your Freightliner data and see it normalized in 30 seconds. No POC required.

Read-only. Forge reads your Freightliner data through standard industrial protocols. Read-only. Your equipment is never at risk.

The problem

Why Freightliner data is hard to integrate

Diagnostics live as raw J1939 SPNs on the CAN bus. Engine oil pressure, turbo RPM and brake air pressure are just numbered parameters until something decodes and names them.

Typical Freightliner equipment communicates over J1939 (CAN), HTTP / REST. 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.

The solution

One universal schema for every Freightliner vehicle

Forge resolves Freightliner telemetry into Forge's canonical field set, the same 1,027-field canonical schema (366 resolvable from vendor tags today), used across all 47 vendor packs. Point Forge at the vehicle, pass oem: "freightliner", and spindle speed is spindle_speed_rpm whether it came from a Freightliner controller or anything else on the floor.

J1939 (CAN)HTTP / REST

91%Field coverage

11Canonical heavy vehicle / off-highway fields

3,444Corpus mappings

Field coverage

What Forge resolves for Freightliner

Confirmed canonical fields your agent gets from Freightliner vehicles, drawn from Forge's 3,444-mapping corpus:

vehicle.engine.oil_pressurevehicle.engine.oil_tempvehicle.engine.fuel_rail_pressurevehicle.engine.intake_manifold_pressurevehicle.turbocharger.rpmvehicle.transmission.oil_tempvehicle.brake.air_pressurevehicle.brake.primary_air_pressurevehicle.wheel_speed_flvehicle.tire_pressure_flvehicle.dtc_description

Sample normalization

Raw tag in		Canonical field out	Meaning
SPN_100: 54	→	vehicle.engine.oil_pressure: 54	oil pressure
SPN_175: 99	→	vehicle.engine.oil_temp: 99	oil temp
SPN_103: 79000	→	vehicle.turbocharger.rpm: 79000	turbo rpm
SPN_1636: "P0299"	→	vehicle.dtc_description: "Turbo underboost"	active DTC

30-second deployment

Add Freightliner to your agent with one call

No SDK, no per-tag mapping, no historian project. Connect the MCP server and normalize live Freightliner data:

claude mcp add --transport http forge https://mcp.foundrynet.io/mcp --header 'Authorization: Bearer YOUR_API_KEY'

# normalize raw Freightliner telemetry into the universal schema

curl -s https://forge.foundrynet.io/v1/normalize \

-H 'Content-Type: application/json' \

-d '{"data": {"SPN_100": 54, "SPN_175": 99, "SPN_103": 79000}, "oem": "freightliner"}'

{"vehicle.engine.oil_pressure": 54, "vehicle.engine.oil_temp": 99, "vehicle.turbocharger.rpm": 79000, "coverage": "100%", "confidence": 0.97}

Unknown tags never get force-mapped. Fields below the confidence floor abstain rather than guess, so your agent never acts on a bad reading.

Start free: Forge API, 3 machines → See the full demo →

Where Freightliner fits

Protocols
J1939 (CAN) HTTP / REST

Vertical
heavy vehicle / off-highway

Other heavy vehicle / off-highway vendors
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