How do you know the AI you deployed actually works?
You're putting models and agents into credit decisions, underwriting, hiring, and clinical support. When your board or your supervisor asks how well they perform, the vendor's own numbers aren't enough. Sovrano AI is the independent party that measures your AI on the real European regulated work you use it for, graded by domain experts, with evidence you can put in front of a risk committee.
Your vendor grades their own homework.
The company that sold you the model, or that built your agents, is also the company telling you how good they are. That is fine until someone with authority asks you to prove it, or until you decide to scale a fleet of agents across the business and want to know they work before you commit. At that point a vendor benchmark is not enough, and generic accuracy scores say nothing about whether the system does the regulated job in your market and your language.
The trigger
Someone asks, or you're about to scale
A supervisory inquiry, an internal audit, a high-risk sign-off, or the decision to roll a fleet of agents out across the business. The question is always the same: how do you know this works, and can you defend that answer.
The gap
Generic scores miss the job
A model can top a public leaderboard and still fail on Spanish consumer-credit rules or a French insurance disclosure. Capability in the abstract is not capability in your regulated workflow.
The exposure
No independent record
If the only evidence of performance comes from the vendor, you carry the deployment risk with nothing neutral to point to when it matters.
Independent, expert-graded evaluation of the AI you run.
Sovrano AI measures how well your deployed models and agents perform on the regulated work you actually use them for, scored by domain specialists in finance, law, insurance, and medicine from a European expert network. You get a verdict, a failure analysis showing where and how the system breaks, and a documented record you can hand to a risk committee, an auditor, or a supervisor.
Door 1 · Measure
You give us access to the model or agent under NDA. Our expert Scholars grade it against a held-out set of real European regulated tasks that never leaves our side, so your system cannot be tuned to the test. You get the score, the failure analysis, and the documented evidence. You never lose control of your data.
Door 2 · Improve
Where the evaluation finds weak spots, we can author new expert training data to fix them: worked examples, preference comparisons, and reasoning traces in the domains where your system underperforms. This is optional, and it is walled from the evaluation set, so the measurement stays honest.
The one thing your model vendor cannot offer you.
Almost every company in AI evaluation also builds or trains models. They cannot grade your system without grading their own work at the same time. Sovrano AI does not build your models and does not sell you a platform to run yourself. We are the neutral party, and we keep it that way on purpose.
We don't build your model
No conflict between the thing being measured and the party measuring it. That is the whole point of an independent verdict.
The wall rule
Evaluation data and any training data we produce for you are kept separate. We never train on, or sell data derived from, the held-out evaluation set.
Held-out by design
The scored tasks stay on our side and are refreshed over time, so a model cannot be quietly fitted to the test between reviews.
The same method underpins EuroExec, our public benchmark of frontier-model performance on European executive work. The public leaderboard shows how the method reads across the major models; your engagement is the private, contamination-safe version run on your own systems.
Evidence for the August 2026 deadline, without the hand-waving.
If you deploy AI in an Annex III high-risk use case, credit scoring, insurance pricing, or employment among them, the AI Act obligations around risk management, data governance, accuracy, and human oversight apply from August 2026. For most enterprises acting as a deployer, you self-assess, and that is valid. So this is not about buying your way out of a fine. It is about clearing that bar defensibly, and knowing where your system falls short before a supervisor or your board asks.
What we are, and what we are not. Sovrano AI is not a notified body and we do not certify conformity. We produce the independent, expert-graded evidence and the provenance record that make your own compliance case defensible. Where our evaluation maps to specific obligations:
Article 9
Risk management
Recurring re-evaluation and drift detection feed a continuous risk-management record for the model in production.
Article 10
Data governance
Provenance and consent documentation for any expert data we produce, generated from the evaluation metadata itself.
Article 14
Human oversight
Expert human review and structured failure analysis you can cite as evidence of meaningful oversight.
Article 15
Accuracy & robustness
Domain-specific accuracy measured on real regulated tasks, with per-item grades and agreement statistics.
Article 27
FRIA support
Technical input for the fundamental-rights impact assessment that deployers of high-risk systems must complete.
GDPR
Provenance
Consent-chain and data-protection documentation on the expert data, with EU-resident evaluators throughout.
Regulated European enterprises deploying AI in the work that matters.
The organizations
Banks, insurers, and healthcare groups first, and any regulated European enterprise putting models and agents into supervised decisions, large employers automating hiring among them. The ones already used to independent model validation from the Basel and Solvency world, now facing the same expectation for AI.
The people
Heads of model risk, chief compliance and data-protection officers, and the AI and transformation leads industrializing agents inside the business. Whoever has to answer for the system when it is questioned.
Where it fits
Alongside your existing model-risk and validation function, not instead of it. We are the independent measurement layer they can commission and cite, the same way they already commission independent validation for credit and market-risk models.
When it starts
Usually when a specific system is going live in a high-risk use case, when a supervisor or auditor has asked a question, or when a new fleet of internal agents needs a performance baseline before it scales.
Not sure it's you? Here's how to tell.
The trigger is not whether you use AI. It is whether AI is making or shaping a decision someone could challenge. Most enterprises are more exposed than they think, and a few are less.
This is you
AI in a decision that counts
Models or agents that decide or materially shape credit, hiring, pricing, eligibility, claims, fraud checks, customer advice, or clinical triage. If a customer, an employee, or a regulator could contest the outcome, you have to be able to defend it.
Probably not yet
Pure productivity use
AI used only to draft, summarize, translate, or write code, with a person still making every decision that counts. A coding assistant or an FAQ chatbot is not the trigger on its own.
The grey zone
The assistant that became the decision
A support agent that now commits the company. A screening tool that filters candidates before a human looks. If you're scaling agents into the business, you're probably here and may not have named it yet. This is the conversation to have.
A verdict you can defend, not a dashboard you have to interpret.
Every engagement returns an evaluation report written for a risk and compliance reader: the score, where the system fails and why, and the documented evidence trail. A sample of the shape:
The deployed credit-advisory agent was evaluated on 420 held-out tasks drawn from Spanish and German consumer-lending workflows, each independently graded by two domain specialists against a shared rubric (Krippendorff's alpha 0.81). Overall expert-graded accuracy was 73 percent. Performance held on affordability assessment but dropped sharply on regulated disclosure wording and on cases involving vulnerable-customer handling, where 6 of the 10 lowest-scoring items clustered. Full per-item grades, rater IDs, and the failure taxonomy accompany this summary.
{
"item_id": "credit_de_0188",
"domain": "consumer_lending",
"jurisdiction": "DE",
"task": "affordability_and_disclosure",
"rater_ids": ["ev_08", "ev_23"],
"expert_score": 2,
"max_score": 5,
"agreement": true,
"failure_tag": "regulated_disclosure_omission",
"rationale": "Agent computed affordability correctly but omitted the mandatory pre-contractual APR disclosure.",
"rubric_version": "v2"
} Illustrative only. Your systems, domains, jurisdictions, and rubric are whatever the engagement needs. Numbers shown are not from a real client.
Three ways to work with us, each scoped to your system.
Most enterprises begin with a single scoped evaluation of one system, then move to a standing oversight relationship once the first verdict proves its worth. Every engagement is fixed-price and quoted after a short discovery call. We do not meter by the hour.
| Engagement | What it is |
|---|---|
| Independent Evaluation | A scoped, expert-graded assessment of one deployed model or agent against real regulated tasks in your domain and market. Score, failure analysis, and documented evidence. The usual entry point. |
| Oversight Retainer | Recurring re-evaluation, drift detection, and human-oversight documentation for a system in production, feeding a continuous Article 9 risk record. |
| Provenance & Documentation | AI Act Article 10 and GDPR provenance and consent packages, generated from the evaluation metadata. Standalone or added to an evaluation. |
FRIA technical support and commissioned improvement data (Door 2) are scoped separately. We price at or above the market for expert-tier work. The value is the independence and the evidence, not the lowest rate. Scope and a fixed quote come out of the discovery call.
What model-risk and compliance teams ask first
Does the AI Act actually require an independent evaluator?
For a deployer such as a bank or insurer, no. The obligations apply when your use case falls under Annex III high-risk, credit scoring, insurance pricing, and employment decisions being the common ones. Where it does, the Act puts the obligations on you to show the system is fit for use, monitored, and overseen, and for most of these use cases you self-assess. Independent, expert-graded evidence is the strongest way to meet and defend that, not a legal mandate we are inventing. We are clear about the difference.
Do you get access to our model weights or customer data?
No. We evaluate through a controlled endpoint under NDA. We do not need your weights, and we do not need real customer records to run a scoped evaluation. Your systems and data stay on your side throughout.
How is this different from the vendor's own benchmarks?
We did not build your model and we do not sell you one, so we have no interest in the score coming out high. That independence, plus expert human grading on real regulated tasks in your market, is exactly what a vendor's self-reported numbers cannot give you.
If you also produce training data, are you still independent?
Yes, because we wall the two apart. The tasks we score you on are held out and never used to train, and any improvement data we author for you is kept separate from that evaluation set. We will not blur the two, because the moment we did, the verdict would stop being worth anything.
Who are the experts?
EU-resident graduate-level specialists in finance, law, insurance, and medicine, recruited from universities across Europe, trained on the rubric before production work, with quality review built in. The same network behind the EuroExec public benchmark.
How do we start without a large commitment?
A scoped Independent Evaluation of a single system is the usual entry point. It is fixed-price, time-boxed, and gives you a real verdict you can take to your risk function before deciding on anything standing.
Start with one system and one question.
The fastest way in is a short discovery call: tell us which deployed system you need a defensible read on, and we'll scope a fixed-price evaluation.
Start here
A scoped evaluation
One model or agent, one regulated workflow, expert-graded on real tasks. A verdict and failure analysis you can take to your risk committee.
Then
A standing oversight relationship
Once the first evaluation proves out, move to recurring oversight and a continuous compliance record for the systems that stay in production.
You're on the Sovrano AI team's desk
A reply follows within one business day. What happens next:
- Discovery call: a short call to scope the system, domains, and jurisdictions for one evaluation.
- Fixed quote: a single fixed-price scope for the Independent Evaluation, and a start date.
Most enterprises start with one scoped evaluation before moving to a standing oversight relationship.
The independent read on whether your AI is good enough to trust.
Expert-graded evaluation of the models and agents you deploy in European regulated work, with the evidence trail to defend them. Independent by design, GDPR-native.
Book a discovery call