Industrial AI · Water-Tech · AI Governance
artxdata designs custom AI for water, energy and industrial operations: forecasting, process optimization, GenAI and governed, EU AI Act–ready systems. Every model is built from your own data, like a commissioned piece.
The name
Rigour first. Sensor streams, panel data and documents are modelled with the tools of econometrics, machine learning and reinforcement learning, then validated with the people who run the plant.
Craft second, never missing. No off-the-shelf template: each model, dashboard and visual is shaped to one site, one process and one team, and can even become a custom artwork made from your own data.
The X is where the two meet: scientific models that operators can read, trust and act on.
01 · Industries
Start from your sector and its constraints. Each industry maps to the problems it faces, the AI that answers them and the use cases already delivered.
Challenges
What we deploy
Challenges
What we deploy
Challenges
What we deploy
Challenges
What we deploy
Challenges
What we deploy
Challenges
What we deploy
02 · AI Solutions
Six service lines cover the full path: choose the right problems, build the models, put them into operation and keep them compliant.
Identify, size and rank use cases against your data maturity and operating constraints; build the roadmap and the business case with plant and business owners.
Custom time-series models that forecast water quality, fouling, scaling and equipment behaviour early enough for operators to act.
Reinforcement learning and micro-econometric models that recommend set-points, chemical doses, cleaning schedules and energy use under real operating constraints.
LLM assistants and agents grounded in your documents, procedures and plant history: document indexing, sentiment services, operator copilots and workflow agents.
AI governance frameworks, risk classification, impact assessment and lifecycle monitoring aligned with the EU AI Act and ISO/IEC 42001, 42005 and 23894.
Pipelines, data quality and model operations on AWS and Spark, from UAT to production, with documentation and architecture your teams can maintain.
Signature service
A year of a plant's operation, a network's flows or a company's history, rendered as an original artwork or editorial visualization. Each piece is generated from real data and can be printed for offices, reports and events.
03 · Technologies
We apply scientific methods to industrial problems. Each method is chosen for the physics of the process, the data available and the decision it must support.
Governance is built into delivery: each model ships with its risk classification, technical documentation and monitoring plan. RO performance data is normalized following the ASTM D4516 practice before any model sees it.
Every model begins with the physics and chemistry of the unit operation: what drives permeability loss, which variables the operator actually controls, which are disturbances.
Econometric identification and controlled back-testing show the effect of a dose or set-point change, net of temperature, feed quality and load.
Forecasts carry confidence intervals and recommendations carry a risk bound, so operators know when to trust the model and when to override it.
Test cases are run with plant operations in UAT before production, and model drift is monitored against the same KPIs afterwards.
04 · Use Cases
Every card below comes from a real project. Filter by industry to see the problem, the method and the data it runs on.
Recommends coagulant and antiscalant doses in real time to cut chemical use while keeping treated water on specification.
Forecasts salinity, UV transmittance and turbidity (NTU) so operators can anticipate changes in feed water.
Computes normalized permeate flow, salt passage and ΔP (ASTM D4516 practice), so membrane decline is visible independently of temperature, pressure and feed salinity.
Detects past clean-in-place (CIP) events in the historian and recommends the next CIP date from the normalized performance trend.
Separates the three fouling mechanisms on RO trains using normalized ΔP, salt passage, SDI and scaling indices, and forecasts each one before it costs production.
Models the energy consumption of treatment plants and finds operating points that use less energy for the same output.
Flags abnormal behaviour in operational and business data so teams investigate the few cases that matter.
Automates supply chain risk assessment with an AI risk methodology and responsible-AI compliance support.
AI-based fraud analysis with a risk management framework and governance processes around the models.
Links quality losses to their likely causes and recommends corrective actions.
Scores supplier failure, stock shortage, antitrust, corruption and fraud, and cyber-security risks through one API.
Builds the risk register, values operational risks from audit reports and recommends risk scenarios.
Identifies, classifies and mitigates the risks of AI products across their lifecycle, with AI Act evidence.
Multidisciplinary econometric and ML specifications to detect fraud in international transfers (ISO 20022) and card payments.
Surfaces communication patterns that may signal antitrust or conduct issues.
Predicts credit risk and prices, and analyses customer behavioural tendencies.
Measures customer satisfaction in real time from free text with an LLM and NLP service (SENT-AI).
Indexes, classifies and scores documents and contracts, including table extraction and compliance scores.
Recommends products and optimizes prices for luxury and tourism offers.
05 · Results & Case Studies
Four engagements show the range, from real-time plant optimization to enterprise AI governance.
Water · Desalination
Veolia Water Technologies · 2021 – present
Desalination and treatment plants run under tight quality limits, with chemical, energy and membrane costs that react to changing raw water.
Governance · Water utility
Veolia · AI Act readiness
A growing portfolio of AI products needed consistent risk classification before the EU AI Act applies.
Enterprise risk · SaaS
Arondor · FERMA expert team · 2019 – 2021
Risk registers were built and valued by hand, from long audit reports, and updated too rarely.
Pharma · Automotive
IPSEN · FORVIA Faurecia
AI used in supply chain and fraud controls had to be assessed and governed like any other critical system.
06 · Expertise
artxdata brings together four competencies that are rarely found in one team.
Econometrics, reinforcement learning, forecasting and custom mathematical models, designed for the process at hand and validated against plant data.
Real-time pipelines, MLOps and technical documentation, from UAT to production, built with plant operations, data engineers and product teams.
Risk classification, impact assessment and lifecycle monitoring aligned with the EU AI Act, ISO/IEC 42001 and 42005, ISO/IEC 27001 and 27005.
Results made readable for operators and managers, and custom artworks generated from a client's own data.
07 · Contact
A first call is enough to see whether a use case is worth modelling. We work in French, English and Turkish.