Weather
~10%
A model built only for weather now beats the physics-based system it learned from on most measures, with rain forecasts about 10% better.
Source: ECMWF, 2025Sovereign AI
01Vertical foundation models.02Own your intelligence.03Air-gapped if needed.
Models built for one field’s work, running where your data lives, on your own hardware or a network with no internet connection. They improve on your data, and you own them and everything they learn.
Own your intelligence
Most companies rent a general model and send it their data. The models that matter most to your business should be yours.
| Question | Rented general AI | Your own model, with Vento |
|---|---|---|
| Where it runs | The provider’s cloud | Your servers or data center, or a network with no internet connection |
| Who sees your data | The provider | Only the people you allow |
| What it returns | Paragraphs of text | A decision for your task, with how confident it is |
| When it changes | When the provider updates it | When you decide |
| What it learns from your work | Nothing you keep | Every correction, and it stays yours |
| How you pay | Per use, for as long as you use it | For the build, then it runs on hardware you control |
Keep renting frontier models for open-ended problems. Own the specialized models that encode how your business works.
Why vertical models win
Across very different fields, a model trained for one domain beats far larger general models at that domain’s work.
Forecasting
1M
A forecaster with about a million parameters beats pre-trained models up to 300 times its size on data it has never seen, and runs without a GPU.
Source: IBM Research, 2024Software tools
7B
A model trained for one job, writing API calls, beat GPT-4 at that job and made up far fewer calls that do not exist.
Source: UC Berkeley, 2023Business tasks
+10 pts
Across 31 tasks, small models each trained for a single task beat GPT-4 by 10 points on average.
Source: LoRA Land report, 2024What Vento does
We build custom models for your use case.
Bring one workflow. We train a model on your own data for that one job, and show it beating a general model on your real examples before anything goes into use.
- You pick the workflow and share real examples of it
- We train a custom model for it and test it against a general model
- It goes into use only if it wins, and the model is yours
What a vertical model can do
Six examples. Each one learns a single job from your own data, and can run where that data lives.
Energy and utilities
Drone inspection
Trained on your own inspection footage, it spots cracks, corrosion and hot spots on turbines, lines and panels, and marks where they are. It can run on site, with no connection.
DecidesDefect type and location
Agriculture
Crop health from the air
Trained on drone images of your own fields, it maps stress, pests and gaps row by row, so crews go where they are needed.
DecidesCondition of each row
Manufacturing
Defects on the line
Trained on images from your line, it passes or rejects each part and names the defect, within the time the line allows.
DecidesPass or reject, and why
Trade and logistics
Tariff classification
Trained on your product records, it suggests the tariff code for each part, with the reasons a customs reviewer can check.
DecidesTariff code, with reasons
Energy
Failure forecasting
Trained on your sensor history, it warns which pumps, turbines and motors are likely to fail, so maintenance is planned, not urgent.
DecidesLikely failures, by asset
Insurance
Claims triage
Trained on your past claims, it approves, sends for review or flags each new one, with its confidence and the reasons.
DecidesApprove, review or flag
The air-gapped economy
Built for organizations whose most valuable data cannot leave the building:
- Defense and aerospace suppliers
- Banks and insurers
- Hospitals and health systems
- Energy and utilities
- Manufacturers
- Public agencies
What we build
A vertical foundation model, deployed where your data lives, and owned by you.
A vertical foundation model
A foundation model specialized for your industry’s work, learning your tasks from a handful of examples. On those tasks it can match a general model with far less compute, and it returns a decision with its confidence, not a paragraph.
For exampleA claims model that approves, reviews or rejects; a tariff classifier for parts; a failure forecaster for pumps and turbines.
Air-gapped by design
Models, search and the tools to run them, installed on your servers, in your data center or in an air-gapped network. Access controls, logs and the documents your security team needs to approve it.
For exampleA plant network with no internet, a hospital data center, a defense supplier’s secure enclave.
Owned and improving
Your workflows become tests, your experts’ corrections become training data and production misses become improvements. The model and everything it learns are yours.
For exampleWhen a newer base model arrives, your tests and training data move to it, so nothing starts from scratch.
How it works
A loop that turns your experts’ knowledge into a model you own, and keeps it improving.
- 01Capture the workReal examples and the answers your experts expect become tests the model must pass.
- 02Train for your fieldA model adapted to your domain, measured against those tests and against a general model.
- 03Run where your data livesDeployed on your hardware, online or fully offline.
- 04Learn from useCorrections and misses in daily work become new training data.
- 05Carry it forwardWhen a better base model arrives, your tests and data move with you.
Then again, with every correction your team makes.
Start with one task
Bring one recurring decision and real examples of it. We will show what a model of your own can do on hardware you control.