The Collective: train an AI without sharing your data
Collective intelligence, everyone's confidentiality. Join a circle of actors who jointly train an AI model: an AI trained by the collective, without any data ever leaving anyone. The model travels, end-to-end encrypted; your data stays home.
Estimate the value: ROI calculator · How you start: how a project works.
Who is it for?
The Collective is for organizations that share a common blind spot but can't (or won't) exchange their data:
- Manufacturers doing predictive maintenance, where each plant instruments only part of the sensors.
- Design / R&D / Product departments of large groups who want to learn from signals measured by their peers without exposing their own measurements.
- Members of a federation, cluster or consortium (industry, aerospace, automotive, energy, pharma) seeking a collective benefit without pooling the data.
The problem: alone, your model is half-blind
Your most useful data never leaves home: too sensitive, too regulated, impossible to hand to a competitor. Everyone wants the shared model; nobody accepts handing over data to get it. The project stops before it starts, and everyone keeps training their AI in their own corner, with a partial view.
The problem isn't the quality of your model: it's its field of vision. Your peers measure variables you don't instrument. Their AI sees signals yours ignores, and vice versa. Alone, you're half-blind, without knowing it. The result: cases your model declares healthy… that break.
How does it work?
How do you train an AI without sharing your data?
With Mesh, training runs at each participant, on its own data. Nothing is centralized. Only the model updates (the weights) travel, end-to-end encrypted, to a coordinator that aggregates them without ever seeing the data. Everyone gets a shared model, better than their own, then can fine-tune it privately.
The three steps
- Everyone keeps their data. Training runs locally, in your infrastructure. No central database, no line of data emitted.
- Only the model travels, encrypted. End-to-end encryption (E2EE) on the transport of the weights. The coordinator aggregates the contributions without accessing the raw data.
- The collective improves. You leave with an engine enriched by everyone's experience (impossible to get alone) and you keep fine-tuning it privately on your own data.
Is my data pooled?
No. The data is neither copied, transferred, nor gathered. The model is trained by the collective, not on a common pot of data. It's an essential technical distinction: what travels are the model weights, encrypted, never your data.
The benefit
- A model better than yours, because it integrates the signals measured by your peers.
- Zero exposure: your data doesn't move; collaboration becomes possible where it was legally blocked.
- A head start for founding members, who shape the standard and get the best terms.
- Easier compliance: since raw data is neither copied nor transferred, exposure is minimized by design (final compliance depends on each project's governance).
The proof
An interactive demonstration exists: access on request. Mesh replays it on real, public datasets, not just synthetic ones demo · real data:
- Industry / predictive maintenance: AI4I 2020 (UCI): the reference dataset, same variables as the field (air/process temperature, rotational speed, torque, tool wear). Three "plants" each instrument only part of the sensors.
- Pharma: Tox21 (NIH/EPA/FDA): 12 toxicity assays split across "labs" that haven't all tested the same endpoints.
- Energy: EDP Wind SCADA: wind-turbine SCADA with labeled failures, partitioned by farm/operator.
On these scenarios, the collective model gains 6 to 8 AUC points over the average of the standalone actors, in all twenty-one runs of the bench: it exploits signals a single actor couldn't see. And federating rather than gathering all the data in one place costs nothing measurable: 0.3 of an AUC point at most, against a measurement uncertainty at least six times larger. Giving up centralization gives up nothing.
As an indication, our shared-sensor demonstration shows +5.6 points of accuracy vs the best single actor. demo: shared-sensor scenario · synthetic data
See also the pillar Federated learning and How to train an AI without sharing your data.
Frequently asked questions
With The Collective, can my competitors see my data?
No. Nobody sees your data: not your peers, not the coordinator, not Mesh. Only the model weights travel, end-to-end encrypted. Your data stays in your infrastructure.
Do I have to be in the same sector as the other members?
No. A collective can be cross-industry: peers from different backgrounds often share the same blind spot. It's precisely this diversity that produces a model none of them would get alone.
How many members are needed to start?
The value of The Collective appears from three actors on. Below that, the collective contribution is too small to be significant, which is why we often bootstrap via existing federations and consortiums.
How is this different from putting the data in a clean room?
A clean room gathers the data in a third-party environment. Mesh gathers nothing: computation stays local at each actor and only the model weights travel. The data is never brought together.
Is it GDPR-compliant?
Federated learning strongly reduces the exposure of personal data: the raw data is neither copied nor transferred. This helps with compliance, particularly for cross-organization collaborations on sensitive data. Final compliance depends on each project's implementation and governance.
Take action
In 90 seconds, with figures, we show you how a model trained by the collective gains 6 to 8 AUC points over the average of the standalone actors, without any data ever leaving home and without losing anything to centralization.