Self-Sovereign AI
Own the intelligence your business runs on.
Your data, open-source and open-weight models adapted to it, agents that run on them, and a learning loop your people approve. In your cloud account or on your own hardware.
The program
Sovereign by default. Dependent by choice.
Self-Sovereign AI is AI your business owns: your data, models adapted to it, agents that run on them, and a learning loop people approve.
Sovereignty is about who owns the system, not where it runs. An owned model can live in your cloud account, on equipment you buy, or in a hybrid arrangement with rented models.
The program is how Sophrono helps a business decide which parts of its AI to own, build them, and keep them improving. Every decision is measured against acceptance criteria your team approves.
Read the plain-language definition, including what self-sovereign AI does not mean.
The question every AI budget eventually asks
A rented model API is an efficient way to start. It gives a team capable models on day one, with no equipment and no training run.
Real work raises new questions: who decides when the model changes, where the data goes, and what each accepted outcome costs at your volume.
Self-Sovereign AI answers those questions task by task. Some tasks move to a model you own. Others stay on a rented model because it measures better for that work.
Own what compounds. Rent what measures better. Decide task by task.
- Which tasks carry enough volume to repay an owned model?
- Which data must stay inside a boundary you control?
- Which workflows need a model that changes only when you approve?
- Which tasks measure better on a rented frontier model today?
Why ownership compounds
Each accepted piece of work can make the next version of the system better at your business.
Your work becomes an asset
Reviewed outputs and corrections become training and evaluation examples. Your team approves each one and records its permitted uses.
Your test set outlasts any model
An evaluation set built from your work measures every future candidate, open or rented, on the same terms.
Change happens on your schedule
An owned model changes when your approver releases a new version. You also choose where it runs and what it runs on.
The commercial measure is cost per accepted outcome: what one piece of work costs once a person has accepted it. Ownership earns its place when that number, and the control around it, clear your bar for a specific task.
Five pillars
Five parts of an owned system
Each pillar can stand alone. Together they form a system that improves under your control.
Your data
Reviewed examples, documented rights, and an evaluation set drawn from your own work.
Your models
Open-source and open-weight models adapted to your tasks, with weights you keep.
Your agents
Workflows on models you control, with people approving consequential actions.
Your learning loop
Reviewed work becomes evaluation data. People approve each release.
Your hardware
Equipment sized from measured workloads. You buy it and you own it.
How the program runs
Every engagement starts with one workload and one question: does ownership fit this work?
- EvaluateAssess one workload for AI fit and the right next step.
- PrepareGather reviewed examples, document rights, and hold back a test set.
- Adapt and testTrain or configure candidates and measure them on held-out work.
- ApproveYour named approver reviews the evidence and releases a version.
- OperateRun the system, capture reviewed work, and repeat the loop.
The first step is the free AI Workload Evaluation for one workload. It tells you whether AI applies and what to do next, including when to keep what you have.
When working code would help you decide, the Agentic Engineering Diagnostic is $750. It includes one hour of agent coding, delivery of the code, an Agentic AI Blueprint, and one hour of consultation.
Builds are scoped as fixed-price or hourly work, with senior engineering at $375 an hour. Full engagement terms are finalized in a Master Services Agreement.
A learning loop with people in charge
- 01WorkCapture outcomes
- 02Human checkpointReviewPeople approve examples
- 03LearnCurate the dataset
- 04RetrainVersion the candidate
- 05TestEvaluate held-out work
- 06Human checkpointReleasePeople approve deployment
Self-improving here means improving through changes people approve. Reviewers accept each training example. A named approver releases each new version after it clears the held-out tests.
The previous accepted version stays ready as the rollback route. See how the loop works.
Governance you can show an auditor
Ownership is useful when it is written down. Each owned model carries a record of what it is and who approved it.
Rights and permitted uses
Every dataset records its source, version, and the uses your data owner approved. Examples without clear rights stay out.
The Model Passport
A record of the base model, its license, the data versions, evaluation results, known limits, and release approvals.
Named approval points
People approve training examples, consequential agent actions, and every production release. The approvers are named in advance.
Terms in the agreement
Ownership, license, and portability terms are set in the Master Services Agreement. The base model's own license also applies.
Where it runs is your choice
Sovereignty describes control, not location. Choose the environment from measured requirements.
Your cloud
Fits teams with an established cloud account. You keep the data boundary and budget for ongoing compute.
Hybrid
Owned models handle selected tasks; approved external services handle others. Each data transfer needs an agreed boundary.
Your hardware
Fits local processing and infrastructure ownership requirements. Capacity, maintenance, and release approval need a named owner.
Compare the three options, and the data boundary that comes with each, in Your cloud or your servers. The principle behind it is Canon rule IX: Infrastructure should fit the work.
We'll tell you when not to own it.
Some tasks are best served by a frontier model you rent. When the measurements say so, we say so, and the recommendation stands.
Our role is to help you decide which dependencies are worth keeping. You buy only the build or the equipment that the evaluation supports.
Already running on an API? Graduate from the API task by task, and keep it where it measures better.
Models for the work you need to do.
The directory is being prepared. We help with frontier models across task types, including open and closed models. Start with one workload evaluation.
Questions
Do we need our own hardware?
No. Owned models can run in your cloud account, on equipment you buy, or in a hybrid arrangement. The choice follows your measured requirements.
Does self-improving mean the AI changes itself?
No. People review the examples it learns from, and every candidate is tested on held-out work. A named approver releases each version.
Who owns the model?
Ownership and license rights are documented for each engagement and set in the Master Services Agreement. The base model license also applies.
Are open-source and open-weight models good enough?
It depends on the task. We measure candidates against your acceptance criteria and recommend whichever option clears them, open or closed.
Can we keep using frontier model APIs?
Yes. A hybrid arrangement keeps rented models for the tasks where they measure better, within an agreed data boundary.
What happens after launch?
Your team can run the system, or AI Stewardship can support it with named human approvers. Either way, releases need a person’s approval.
Keep exploring
What is self-sovereign AI?
A plain-language definition, a rented-versus-owned comparison, and who it fits.
Your cloud or your servers
Cloud account, hybrid, or your own hardware, with a documented data boundary.
Graduate from the API
Move from a rented model to an owned one, one measured task at a time.
Model Training & Fine-Tuning
Adapt a model to your work, with an evaluation set you keep.
AI Stewardship
Ongoing operation of the learning loop, with named approvers.
Research
How new techniques are tested before they reach a production system.