Most AI engagements ask you to commit to a programme before anyone can tell you whether it will work. These do the opposite. Each one is a bounded piece of work, priced up front, that ends with a document you own and a decision you can make.
They are also not slide decks. Every one of them involves running something against your real data or your real system, and the findings come with the evidence attached.
Three phases, and you can stop after any of them
Phase 1: Assess. The three fixed-price reviews on this page. Each answers one question about one system and ends in a report, a verdict and the machinery to check it again later.
Phase 2: Build. Projects, meaning custom models and systems, scientific R&D and research partnerships. Where there was an assessment, the project is scoped from it.
Phase 3: Run. Handover, monitoring and retests on a regular cadence, or we keep operating the system we built.
Two things are worth saying plainly, because most consultancies leave them ambiguous. Buying Phase 1 commits you to nothing else: the report is written to be useful if you hand it to your own team or to another supplier, and we would rather you did that than bought a build you did not need. And a project does not require an assessment first. If you already know what you want built, you can go straight to Phase 2.
Three reviews, one question: can this be trusted?
AI Readiness
Before you build. Which of your candidate uses of AI are feasible on the data you have, which are not, and what to do first. Choose between a scan that works from what you can show us and a full assessment that runs baselines on your real data.
Model Validation & Deployment
After you build, against reality. Can you trust the model, and how do you run it in production? We cover calibration, evaluation leakage, shortcut learning, slices and drift, as well as what it takes to serve, monitor and pay for the model once it is live.
AI Trust & Security
After you ship, against attackers. Red-teaming for LLM applications and agents, covering prompt injection, data leakage and tool misuse, plus a white-box review of the code around the model. This is not a pentest: it targets the failure modes that only exist once a model is in the loop.
Two of these look at a system you already run, and what separates them is the failure mode. The security audit asks what happens when someone makes the system fail; model validation asks whether it is failing unnoticed on its own. An LLM is still a model, so a chatbot can need both.
Each comes in two tiers. The entry tier covers one system with the standard battery and ends in a verdict. It costs €4,950 excl. VAT and typically takes two to four weeks. The full tier goes wider and deeper, hands over the harness or the probe suite, and includes a retest. It costs €24,500 excl. VAT and typically takes eight to twelve weeks. Both are fixed price against a written scope, and the number does not move because the work turned out to be interesting.
The clock starts when the data and the access are in place, not at signature, because that part is outside our control. If it turns out there is not enough data to answer the question, we write that up as a finding, stating what is missing, how much would be enough, and what can be said with what exists. It is not left to stall.
What you can count on
Fixed scope and fixed price. You know what it costs before it starts, and the number does not move because the work turned out to be interesting.
A matter of weeks. None of these needs a long programme before it produces something worth having. Bigger work does exist, such as building the system or fixing what a review found, but it is quoted separately afterwards and is never a condition of getting started.
You keep the machinery. The probe suite, the calibration harness and the monitoring checks all go into your repository and run in your CI, so the next model gets the same scrutiny without hiring anyone.
- 1
Scoping call
In half an hour we cover what the system does, what decision depends on it, and what a bad day looks like. This fixes the price.
- 2
Structured intake
Access, data or documentation, as much as the question needs and no more.
- 3
Analysis
A standard battery for the offer, plus whatever your domain specifically demands.
- 4
Report and readout
Findings are ranked by consequence, each with its evidence and a concrete remedy, and presented to your team.
- 5
Handover
The tests stay with you, and a retest follows once you have acted on the findings.
Your data
Handing your data or your systems to an outside party for examination is a reasonable thing to be nervous about, so you choose the level at intake instead of discovering ours afterwards.
No AI on your data
No AI system sees your data or your code at any point. We still use AI to write generic code, as we would with or without you, but it never touches anything of yours.
EU-hosted AI
The default. Your raw data stays with you: the analysis runs on your infrastructure or in an isolated environment, and only structure and results come back. Anything that does go to a model runs in an EU region and is not used to train anyone's model.
Full access
Data and code go through the same EU setup, under a data processing agreement, when you would rather trade that for speed.
Whichever level you pick is written into the engagement, along with where data lives, who can reach it, and when it is deleted. Client data never goes through a personal or consumer AI subscription.
An honest boundary
We are engineers, not counsel. These reviews produce technical evidence in the form of measurements, reproductions and findings, which your legal and compliance people can use and increasingly need. What we will not do is tell you that you are compliant with anything. That signature belongs to someone whose job it is.
What is never included, in any tier, at any price: compliance or legal sign-off, integration of anything into your systems, running or supporting the system afterwards, and fixing what a review found. We are happy to do the last three, since they are real work. They are projects, quoted separately, because pretending they fit inside a fixed-price review is how fixed prices stop being fixed.
Where to go next
Model validation & deployment
For a scientific or industrial model already in use, or about to be.
AI trust & security
For anything with a language model in it.
Projects
When a review says build: custom models, systems and research partnerships.
How we build
The method underneath all of it, and what we do once a review says the system is worth building on.