CROSS-VENDOR · FACTORY FLOOR INTELLIGENCE
19 verticals·55 manufacturer pages·13 protocols·2,086 mappings
Haas, Fanuc, Siemens, KUKA, DMG Mori, Mazak, ABB, Yaskawa. Every OEM speaks a different language. Forge translates all of them into one canonical schema any AI agent can reason about.
Pick your manufacturer below, or paste a raw tag dump and let Forge identify it.
No manufacturer set out to be incompatible. Each one designed a controller, named its registers sensibly for its own engineers, and shipped. Fanuc exposes FOCAS. Siemens exposes S7 data blocks. KUKA exposes KRL variables. Haas buries production state in Q-variables and macro registers. Every one of those decisions is locally reasonable and collectively unusable.
Your agent does not need a better model to fix that. It needs someone to have already done the translation, with the units checked, the scales checked, and the wrong answers withheld rather than guessed.
A selection of the 55 OEM pages with dedicated coverage. Each links to its own tags, protocols and sample normalizations:
Requires spindle load to mean the same thing on a Haas and a Mazak. It does, once both are spindle_load_pct with the unit and scale resolved.
Different cells rarely share a vendor. Normalized fields make a cross-cell comparison arithmetic instead of an integration project.
Cross-correlation across mixed vendors only works when the timestamps and field names line up. Forge emits both.
Fleet-level OEE across vendors is a normalization problem long before it is an analytics problem.
The mechanics: 5 layers in, 4 read-time validators plus an operating-mode classifier out, one canonical schema.
32 tools that give any agent access to every vendor above.
Once the fleet is normalized, prediction stops needing an integration project.
Longer-form writing on industrial AI infrastructure by Foundry Labs.