NEURIXIS helps companies identify, prototype and industrialise the AI use cases that pay for themselves. Built by engineers who ship AI into production, not into slide decks.
The problem
Companies are not short of AI ideas. They are short of a method to separate the profitable use cases from the gadgets, and of engineers who can take a prototype all the way to production. That is exactly what NEURIXIS does: we put a number on it before we build, we build what pays, and we run it inside your systems, with your teams.
The reasons projects fail rarely change. The data is not ready: scattered, poorly documented, with no clear owner. No business sponsor carries the project, so nobody arbitrates when a trade-off has to be made. The proof of concept demos well, then never reaches the information system because no one planned the architecture or the operations. And inference cost is never modelled before go-live, so the bill lands after the decision.
If it isn’t measured, we don’t sell it.
What we do
Business assistants and tool-using agents, connected to your data, with guardrails and traceable answers. We deliver the document indexing, the RAG layer and the evaluation sets that actually measure quality. In insurance, they work up underwriting files and triage claims.
Quality control, detection and document reading across your image and video streams, on the production line. We train models on your annotated images, then deploy them at the edge on GPU or smart camera. In manufacturing, that flags a weld defect before the part ships.
Demand forecasting, predictive maintenance and schedule optimisation, grounded in your historical data. We benchmark time series against gradient boosting, then surface the result inside the tools your teams already use. In retail, per-store per-SKU forecasts drive replenishment.
Pipelines, data quality and governance: the foundation without which no model survives in production. We put in place ingestion, cataloguing, quality tests and access traceability. In healthcare, that means pseudonymisation and certified health-data hosting from the design stage.
Deployment, monitoring, retraining and model versioning inside your information system. We set up the model registry, the CI/CD pipelines, drift detection and inference cost tracking. In logistics, a route optimisation model retrains weekly with no service interruption.
Upskilling technical and business teams, from framing a use case to running it day to day. We train on your own datasets, with hands-on workshops and a use-case qualification grid. In the public sector, that frames how staff use assistants and the AI Act obligations that follow.
Why NEURIXIS
Every engagement starts with a quantified business case and a success criterion written into the contract. If it cannot be measured, we do not sell it. We set the baseline before the first line of code: current handling time, observed error rate, real unit cost. The same indicator, measured the same way, is reviewed at the end, using your numbers, not ours.
Our engineers deploy AI in critical, regulated environments where mistakes are not forgiven. Your project gets that same standard. In practice: automated tests, code review, secrets management, a rollback plan, and continuous monitoring in production. We work inside your toolchain and release process, never alongside it.
European or on-premise hosting, architectures aligned with the most demanding security frameworks, GDPR and AI Act compliance built in. Your data stays yours. We can run open models on your own infrastructure when the subject calls for it. You keep control of the weights, the logs and the processing, and you know where every piece of data sits.
The team
President
Data scientist. Several years at Airbus Helicopters on AI projects in industrial settings, from scoping to production. He advises leadership on project strategy: which use cases to launch, in what order, for what return.
Chief Technology Officer
Data scientist, trained in the demands of industrial environments at Saunier Duval. He owns the technical side end to end: real data, production and monitoring, at the reliability level an industrial site requires.
What decision-makers ask us before they commit.
The scoping workshop runs in a single day, and you leave that evening with a shortlist ranked by estimated gain and integration effort. The first prototype on your real data is a matter of weeks, not months: that is where you get a measured result, held against your starting point. Going into production then depends on your information system and your compliance constraints, and we estimate it at scoping rather than promise it blind.
On your real data, in an isolated environment, from the prototype onward. A demo on a public dataset proves nothing about your case: your data carries your formats, your edge cases and the gaps in your history, and that is exactly what decides whether a model holds. If something blocks access to a real, representative extract, we deal with it before we start, not halfway through.
The success criterion is written into the contract before the first line of code. If it is met, we industrialise. If not, we stop, and you hold hard evidence that this use case does not pay off, which beats a doubt kept alive for two years. A prototype that settles the question in the negative is a useful deliverable, not a failure: it saves you from industrialising a dead end.
We take one project at a time and finish it before starting another. The two engineers who sell you the project are the ones who write the code: no management layer to bill, no handover between a pre-sales pitch and a delivery team that was not in the room. The trade-off is that we say no when we are not the right fit, or when your programme assumes twenty people deployed in parallel across several countries. And we say it at the first meeting, not the third.
Yes. We design for hosting in Europe, on your own infrastructure or on a qualified offering depending on your requirements, and we can run open models on your side rather than call a third-party service hosted outside that perimeter. For the most sensitive sectors, where processing happens is settled at scoping: discovered later, it invalidates an architecture that is already built.
No. Many of the people we talk to have no standing data team, and that is not a barrier to starting. We build with your business teams and your existing IT, and skills transfer is part of the project: by the end, your engineers handle incidents and retraining on their own. If you do have a data team, we work with it, not around it.
It depends on the case, and we decline to quote a figure before looking at it. The scoping workshop is exactly for that: it ranks each lead by estimated annual gain and integration effort, which lets us price rather than announce a flat fee at random. We do not take on a project whose return on investment is not positive within eighteen months: if the numbers do not add up, we say so, and we do not bill work that will not pay off.
The goal is the opposite. We hand over the code, the models, the evaluation sets and the operating documentation, and we train your teams on your own data. Our presence tapers off until it disappears: you must be able to take the service back or hand it to a third party without us. We do not sell a platform and we do not charge a licence fee, the code and the models are yours.
