Shared infrastructure through which industries build an IoT Frontier Model together — learning from machine states, operating conditions, actions and outcomes across enterprises, while every contributor keeps control of what it shares and a stake in what it becomes worth.
A battery storage site, third week of a heatwave. The dispatch software recommends selling into the afternoon price peak. But the operations team has watched the battery for days: under sustained heat, it delivers less usable energy than the software expects. They hold some power back — and when demand peaks again that evening, the site still has its reserve.
A year later, in another city, an operator running the same type of battery faces a similar decision. The first team learned something worth knowing. The second has no practical way to benefit from it.
Under sustained heat, usable capacity falls below what the software expects. Holding reserve protects the evening peak.
Same battery type. Similar weather. Same decision — with none of what Site A already knows.
We have learned how to connect machines across the world. The harder question: how can the experience of operating one machine become a better starting point for the next?
A manual gives rated capacity. A sensor gives temperature. A model recommends when to discharge. A trusted record joins them — and keeps going: what the operator changed, what the controller actually executed, and what the meter actually measured.
The model learns that under sustained heat, usable capacity sits below rating — and that holding reserve protected the evening peak. Its recommendation was corrected, and the record says so.
Without the override, the recommendation appears to have worked. The model credits itself for an outcome it did not produce — and repeats the mistake next summer.
Every observation can be traced back to its source.
A proposed action is never mistaken for an actual one.
Every prediction can be tested against a measured result.
A company can buy more computing power. It cannot buy a second copy of a summer that only one fleet has lived through.— The TLAY IoT Frontier Model thesis
Each party holds something the others need. None should have to surrender its business to contribute. Collaboration becomes attractive when participants agree both on what others may learn — and on what contributors receive in return.
Operators and manufacturers contribute authorized experience. Model teams contribute specialist capabilities. Members run nodes for training or inference. Individual datasets, proprietary models and customer relationships remain under their owners’ control — TLAY makes the cooperation workable.
Identify the machines and participants behind every contribution.
Which data and which model, for which task, used by whom.
Verify how each model version was evaluated before release.
Settle payments for services delivered — checkable by every member.
Members agree in advance on model access, release criteria and revenue-sharing rules — so that no single operator can change them unilaterally. Responsibility for model quality and customer delivery stays with named teams.
The first customer need not wait for a frontier model. Four steps take the network from a useful service today to a model that has earned its place.
A storage operator connects a site to the TLAY AI Platform and uses an existing forecasting model and optimizer to plan dispatch. The operator sets the objective and reserve requirement; the local controller retains safety authority. The customer gets value today — and a baseline any future model must beat.
Before training, the parties agree which data and model may be used, for which task, what may leave the computing environment, and who may use the result. Confidential computing can bring protected data and proprietary weights into a verifiable environment — neither side discloses its asset to the other. A common component might be open; a sensitive capability ships through a controlled API.
Compare the existing service, a model developed by one operator, and a model developed with several. Hold an entire site back from training. Measure dispatch cost there under the same reserve and battery-wear constraints. Only an improvement that survives this test merits wider deployment.
A subscription or paid dispatch request creates revenue that can be reconciled. Node operators are paid for verified work. An operator whose rare heatwave records helped a version pass evaluation earns an agreed share — even if it runs no node. The developer is rewarded for the accepted improvement; TLAY charges for operating the service. On-chain records let members check permissions, versions and settlements without publishing raw telemetry or weights.
One real machine activity can answer three different questions. Each use has its own boundary — and requires its own permission.
The delivery record shows what service should be settled.
With separate permission, a history of performance and cash flows helps a financing partner assess the asset.
With authorization for learning, the gap between forecast and outcome becomes evidence for the IoT Frontier Model.
That gives the next enterprise a reason to join — and gives the IoT Frontier Model a foundation in both industrial experience and real demand.
The operator in the other city receives a forecast informed by performance under sustained heat. Its team tests the forecast against local conditions and chooses how much energy to retain.
It has never seen the first operator’s customer list or trading strategy. Yet a lesson from that first site has travelled. If the second site pays for the improved service, the first can verify its share.
The next machine can begin where another left off — and the people who made the lesson possible keep a stake in what it becomes worth.
Each addition can make a specific capability better, and each improvement is tested in the world where it must work. Tell us where you’d fit.
