Cost
“The bill grows with every new use of AI, and the enterprise is locked to one model's price or one cloud's terms.”
How the stack answers it — Run it →
Our point of view
Open & Sovereign AI
Today's models learned from the public internet — the small part of the world's data. The far larger part is private, and no public model was trained on an enterprise's share of it: its transactions, its client files, the way it makes decisions. The next gains in AI will come from putting models and agents to work on that data.
With open models, no enterprise has to hand that data to someone else to get them. Open models are close enough in performance for most business needs, and their weights are published, so an enterprise can run one on its own hardware, tune it on its own data and own the result. That is open and sovereign AI, and our bet is that the most valuable AI will be built this way.
The definition
Open is how you do it. Sovereign is what you get.
Open weights are how you run a model on your own hardware and tune it on your own data. Sovereignty is what you get out of it: your data, your prompts and what the model learns stay in your company, and the thing that compounds is yours. Our mission: make AI answer to you.
Three levels
Sovereignty comes in three levels
Level 1
Run it yourself
The weights are published, so the model runs on your hardware — your cloud, your data center, fully air gapped. Nobody can cut off your access.
Level 2
Keep the data inside
Your data and your prompts never leave the tenant. Every step is signed and auditable, and agents reach your systems only through governed doors.
Level 3
Own the result
Tune the model on your own data and own what it learns. The capability compounds for you instead of leaking to a vendor.
The crossroads
Three concerns stop a regulated buyer cold
What it costs once it scales. Whether an agent can be caught before it does damage. And who owns the data.
Security
“Agents act at machine speed, faster than any human reviewer. If an agent does something wrong, can you catch it before it is too late?”
How the stack answers it — Protect it →Who owns the data
“When the model and the cloud belong to someone else, it is not clear who owns what the business puts into them.”
How the stack answers it — Store it → Why now — three trends
Open models closed the gap
Smaller open models now reach the quality that used to take the largest ones.
The cost collapsed
A million tokens falls from dollars to cents, and small models run on a PC or a phone.
The value moved to private data
The next gains come from the data your enterprise already owns.
What you get
Three benefits, each proved by the platform
Your AI. Your sovereignty.
Use an open model that publishes its weights, not one that is closed and proprietary. You do not lose your competitive advantage, because your data and your prompts stay in your company.
The stack runs on premises or fully air gapped. Orion's zero knowledge infrastructure keeps data inside the tenant, Carina stores the enterprise's knowledge privately, Vela watches every agent in real time and can stop one before it finishes, and every step is signed and auditable.
Store it — Carina →Lower cost and no lock in.
You can use multiple models, so it is easy to switch if a better one comes along, and no one can cut off your access and stop your business.
Model independence. Orion routes each task to the model that clears the quality bar at the lowest cost, on premises or fully air gapped.
Run it — Orion →Good enough for the enterprise.
Open models are close enough in performance for most business needs. Most businesses do not need an Einstein in every job, and small open models are so good you can run many functions on PCs or even phones.
In our 30-task internal TNE benchmark, a cheaper model with Compass beat the most expensive model run bare. In Compass, every result clears evaluation gates before it reaches a business process.
Build it — Compass →2,500+
agents on the platform
250+
end-to-end processes
+10 pts
accuracy at 38% lower cost — with Compass, on a cheaper model
In our 30-task internal TNE benchmark, a cheaper model with Compass beat the most expensive model run bare.
Use both
Not against the large closed models
Enterprises use them for general work and keep open and sovereign models for their most sensitive and proprietary work. The point is not ideology — it is that the work which carries your competitive advantage should run where you control it.
The fork
Scale up or scale out
Buying commodity AI is more efficient — but so are your competitors. Advantage and profits concentrate at the top of the fork.
Data stays behind the firewall — on your infrastructure, in your nation's data centers, on your PCs and phones. The rest of the world's data is yours. Don't give it away.
For boards and executives
Three things we ask leaders to do
Ask who owns the data
For every AI initiative: who holds the data, the prompts, and what the model learns? If the answer is a vendor, your edge is leaking to them.
Ask what it costs at scale
Price per task, not per seat. And ask whether you can switch models when a better one comes along — or whether your provider owns your pricing.
Ask if you can catch it in time
Agents act at machine speed. Ask who watches them in real time, who can stop one before it finishes, and what record it leaves behind.
Talk to us
The first conversation is about your work, not our software
Tell us the one process you most need to own, and an architect will show you what open and sovereign AI does with it — running in your own environment.
You talk to an AI architect, not a sales rep.