Engine, Ready: a private AI model, ready to deploy
The collective's engine, without giving up a single row. You leave with an AI model already trained by the collective, deployable in your infrastructure. You run it at home, you fine-tune it privately on your own data. The model travels to you: your data doesn't move.
Estimate the value: ROI calculator · How you start: how a project works.
Who is it for?
Engine, Ready is for organizations that want the result without the project:
- Actors with no data to share, or who lack the volume to weigh in a collective.
- Teams that want a high-performing model right now, without building a consortium themselves or orchestrating multiple partners.
- Organizations that require a private / on-premise deployment: the model runs within their walls, under their control.
The problem: building a collective takes time, and you want the engine now
A model trained by a collective is better than a solo model. But forming that collective takes time: finding the peers, aligning governance, reaching the critical mass of three actors or more. Not every organization has the time, the data, or the desire to run this setup.
You want the benefit of the collective (an engine that sees what a single actor can't) without carrying its construction.
How does it work?
How do you get a model trained by a collective without contributing to it?
The model has already been trained by a collective of actors: each on its own data, without ever sharing it (see The Collective). You, you bring no data: you get access to the already-trained engine, you deploy it at home, and you fine-tune it privately on your data if you wish.
The three steps
- You pick up the engine. A model already trained by the collective, ready to use, without your having contributed anything.
- You deploy it privately. It runs in your infrastructure (on-premise or your private cloud). You keep control of it.
- You fine-tune it at home. You can specialize it on your own data, locally: this fine-tuning stays private and goes nowhere.
What is local fine-tuning, exactly?
It's your private work on the engine: you train it further on your data, without anything going out. Not to be confused with the original collective training: the local fine-tuning stays yours, at your place.
The benefit
- The collective's result, immediately: an engine that integrates signals a single actor wouldn't see.
- Zero setup: no consortium to form, no partners to align.
- Private deployment: the model runs within your walls, under your control, with no dependency on a central database.
- Customizable: fine-tune it on your data privately, without ever exposing it.
The proof
An interactive demonstration exists: access on request. The engine is trained on real, public datasets (AI4I 2020 in industry, Tox21 in pharma, EDP Wind SCADA in energy), then can be run locally. 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's exactly the engine you get. demo · real data
For the underlying mechanism, see Federated learning and Confidential AI.
Frequently asked questions
Do I have to provide data to get the engine?
No. That's the whole point of Engine, Ready: you contribute no data. The model has already been trained by the collective; you get access to it, ready to use.
Does the model run at my place or at Mesh?
At your place. Engine, Ready is designed for private deployment (on-premise or your private cloud). You keep control of it, with no dependency on a central database.
Can I adapt it to my specific cases?
Yes. You can fine-tune it on your own data, locally and privately. This work stays with you and is never shared.
What's the difference with The Collective?
With The Collective, you contribute to training and leave with the shared engine. With Engine, Ready, you contribute nothing: you buy access to the already-trained engine. Same engine, different posture.
Does the engine keep improving with the collective after delivery?
The engine you get is a version trained at a given point in time. The terms for later updates (new versions from the collective) are defined within the agreement: we discuss it in a session.
Pick up the engine
We show you in a demo the engine trained by the collective, and how to deploy it privately at your place, without contributing a single row of data.