PREDICTIVE MAINTENANCE · ZERO CONFIGURATION
30-hour lead time·NASA bearing benchmark·Zero configuration·Existing telemetry
Traditional predictive maintenance is an 18-month integration programme with a model at the end of it. Forge runs prediction against the telemetry you already have, on the first call.
No sensors to install, no model to train, no data engineering team to hire.
A conventional predictive maintenance deployment spends 6–18 months on tag mapping, historian plumbing and data cleaning before anyone trains a model. Six figures of consulting go into work that produces no prediction at all — it produces the conditions under which prediction becomes possible. The model at the end is often a commodity.
That is why PdM pilots stall on machine number two. The mapping work does not generalise across vendors, so the second machine costs nearly what the first one did, and the business case dies on the arithmetic.
Degradation patterns validated 30 hours before breakdown on NASA benchmark bearing data, with per-machine calibration. The lead time is what makes it actionable rather than merely accurate.
Estimated life on the asset as it is actually being run, not as the maintenance schedule assumes it is being run.
Cross-machine health indexing so the question "what do I do this week" has a ranked answer rather than a dashboard.
When something is wrong, correlation across sensors produces a plain-language cause, not just a red tile.
Prediction is cheap once the input is consistent. The expensive part of traditional PdM is producing a clean, unit-correct, vendor-neutral time series — which is precisely what the normalization layer already emits. Forge does not skip the work; it front-loads it into a corpus that every customer's second machine benefits from.
The prediction endpoint is stateless and wants a recent window of numeric values in the request body — it is a function of the data you send, which is what makes a first call possible 30 seconds after signup.