AI consulting and engineering · Paris
We find, prototype and industrialise the use cases whose return can be put in figures. By engineers who ship to production, not to slideware.
ROI
Use cases ranked by ROI, never by hype
Criticality
AI deployed in critical, regulated environments
Sovereignty
European hosting, GDPR and AI Act compliance
Handover
Skills transfer to your teams included
Engagement models
The model follows your situation, not our catalogue. If none of them fits, say so — we would rather say so too.
When to choose it
Defined scope, firm deadline
We commit to a deliverable, a price and a date. The success criterion goes into the contract before the first line of code, and it is what triggers acceptance. The default mode for a scoping study, a prototype or an industrialisation whose scope holds.
When to choose it
An existing team to reinforce
One or more of our engineers join your team, inside your rituals and your toolchain. You own the backlog, we bring the missing skill: data engineering, MLOps, computer vision, language models. Senior profiles only — we do not put juniors on critical systems.
When to choose it
Models already in production
A dedicated team keeps your models running over time: monitoring, drift detection, retraining, incident handling. A written service commitment, indicators reviewed every month. This is what stops a delivered model from becoming an abandoned one.
When to choose it
Nothing scoped yet
One day on your site to map your processes and leave with three scored and costed use cases. No obligation to continue. The way in when the question is still where to start.
The problem
80
of AI projects create no value
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.
01
Scattered, poorly documented, with no clear owner: the project opens with weeks of clean-up that nobody had budgeted.
02
Nobody carries the project on the business side, so nobody arbitrates when a trade-off has to be made.
03
The proof of concept demos well, then never reaches the information system, because no one planned the architecture or the operations.
04
Never modelled before go-live, so the bill lands after the decision.
NEURIXIS working principle
The method
Four steps, four deliverables. Each one can be the last: you only commit to the next once the previous has been handed over.
01
Step 01 of 04
We come to you, we listen to your teams, we map your processes. By the end of the day you leave with your three most profitable use cases, scored and sized in orders of magnitude.
Deliverable
A scoping note with your scored use cases, the order of magnitude of the gain, and the technical path to get there.
02
Step 02 of 04
We prototype the priority use case on your real data, with a success criterion agreed in the contract from day one: time saved, error rate, unit cost. The PoC either proves the case or kills it. Both outcomes save you money.
Deliverable
A prototype running on your data, its quantified evaluation report, and a reasoned decision: industrialise or stop.
03
Step 03 of 04
Go-live inside your information system: secure architecture, MLOps, data governance, AI Act compliance. AI that runs every day, not a demo gathering dust.
Deliverable
The service running in your information system, with its code, its deployment pipelines and its compliance file.
04
Step 04 of 04
Monitoring, continuous improvement and training so your teams take ownership of the solution. We are aiming at your autonomy, not your dependency.
Deliverable
A monitoring dashboard, trained teams, and operating documentation your engineers can pick up on their own.
What we do
Six domains covered end to end, from scoping to running in production.
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.
Industries
Manufacturing, healthcare, public sector, insurance: regulated environments where an error does not cost the same depending on which way it goes. That is where our demand for measurement counts most.
In-line quality control, predictive maintenance and forecasting, under cycle-time and CE marking constraints.
View sector
Clinical text structuring, document retrieval and activity forecasting, under HDS hosting, GDPR and medical device constraints.
View sector
Request routing, retrieval over regulatory corpora and archive transcription, under sovereignty, accessibility and reversibility constraints.
View sector
Assisted case handling, audit targeting and risk modelling, with explainability and validation treated as design requirements.
View sector
The team
Two engineers trained in the demands of industrial environments. They are the ones doing the work.
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.
Why NEURIXIS
Three commitments, written into the contract rather than onto a web page.
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.
An AI project to scope?
Book a workshopCase studies
Real projects, anonymised but not sugar-coated: the problem, the approach, the result.
-75%less drafting time
Drafting drops from several days to a few hours. And cited references keep themselves current: when a manual is amended, the file regenerates instead of being re-read line by line. The risk of error is gone.
Read the case
-92%less production time
The right movements are predicted in a few minutes instead of three hours, without the iterations that drove the production bill up. Video becomes serial: properties go live at the pace of the mandates, not of the editor.
Read the case
A high-quality voice, tone and prosody included, near-imperceptible latency, and a proprietary model the client owns: no dependency on a third-party API, no per-minute cost.
Read the case
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.
