Data for AI
No reliable AI without reliable data. Adservio builds your data foundation: quality, governance, lineage and industrialised pipelines, through to the data copilot your teams query.
No quality AI without quality data.
Most of the effort in an AI project goes into the data. Adservio builds the foundations: quality, governance, pipelines and compliance, so your models train on solid ground and hold up in production.
What we do
Each step is an offer of its own, with its own page. They read in order, but a programme starts wherever the gap costs the most.
Data platform & governance
One foundation rather than a sum of silos: shared reference data, infrastructure described as code, traced access and compliance held over time.
See the offerData quality & reference data
A golden record every domain shares, and quality measured on every run rather than audited once in a while, with remediation attached.
See the offerAugmented BI & Data Copilot
A shared semantic model and a copilot that answers in plain language from your own reference data, with the sources attached.
See the offerAI forecasting & anomalies
Models that anticipate demand and flag the deviation before it costs, with retraining triggered by measured drift.
See the offerBusiness copilots & Vision AI
What the analysis recommends becomes a tooled action, in the business tool, with its guardrails and its audit trail.
See the offerMLOps & LLMOps
Feature store, model registry, canary deployment, drift monitoring and retraining: what goes live keeps being measured.
See the offerWhat you get at every step
Data stays where it must stay
Each dataset is stored and processed according to its sensitivity, in the cloud or on your own infrastructure. The routing rule is written at design time, not toggled at the end.
Every figure can be explained
Lineage traces where a number came from and what transformed it along the way. That is what turns a dashboard value into something defensible in front of an auditor.
What goes live keeps being measured
Checks, thresholds, drift and cost are followed after go-live. A flow or a model nobody measures any more degrades without saying so.
What shifted in 2026
Partners
We don't sell licences. The building blocks are chosen for your constraints, never the other way around, and integrated to the same engineering standard whatever the brand.
See the full ecosystemLakehouse, pipelines and feature store on Databricks, from the training set to the model monitored in production.
DatabricksSovereign hosting in France when the data must not leave the territory, on the same foundation and the same deployment chain as the rest.
A European option for infrastructure and storage, chosen when data residency weighs in the architecture decision.
Interfaces and data applications delivered continuously, every change previewed in its own environment before it is merged.
The people you will work with
A data programme is settled in meetings, not in documents. These are the people who run them.
Saif Warradi
AI project manager
Holds the framing and the trajectory of a use case, from prioritisation by value and feasibility through to the go or no-go before industrialisation.
Katharina Flais
Senior Key Account Manager
Your point of contact for the length of the programme: scope, commitments, and the link between your directorates and the Adservio teams.
Michel Sainte-Rose
Delivery Director
Answers for execution: how teams are staffed, which engineering standards hold from one assignment to the next, and what actually reaches production.
Data turned into decisions
Fraud caught in real time, KYC cut by more than half
+40% fraud detection · −60% KYC time
Detect fraud on massive volumes in real time, shorten a KYC slowed down by scattered customer data and manual checks, and make regulatory data reliable, all under heavy compliance and sovereignty constraints.
A governed data foundation with MDM for a single customer reference and quality measured continuously, anomaly detection models wired into the flows, and a data copilot for analysts: sourced, auditable answers that speed up decisions without losing traceability.
Attendance forecast by AI, waiting time cut by more than half
+16% revenue · −58% attraction wait
Anticipate attendance peaks to cut waiting times and optimise revenue, from massive datasets and real-time signals that traditional statistical forecasting does not capture.
AI forecast models trained on history and fed by real-time signals, anomaly detection on the flows, and generative analytics to steer attendance live: resources reallocated, offer adjusted, decisions taken as things unfold.
Insights & Perspectives
Data Mesh: the architecture reshaping enterprise data
Rather than centralising everything in a lake or a warehouse, data mesh hands the data back to the business domains that produce it, as governed products.
Beyond the algorithm: six hidden obstacles to data and AI success
Most AI programmes do not fail on the model. They fail on the six obstacles that sit underneath it, and that nobody measures until it is late.
Data governance in digital transformation
What governing data actually means day to day: ownership, quality, lineage and the arbitration between compliance and the pace the business needs.
The rest of the data and AI journey
ExpertiseGenAI Platforms
A sovereign, governed GenAI foundation: hybrid LLM gateway, RAG platform, LLMOps and AI FinOps.
ExpertiseAI Security & Governance
AI Act and GDPR compliance, explainability, bias detection and continuous supervision.
Give your AI solid foundations
An audit of your data, a governed and compliant foundation, industrialised pipelines.
Frequently asked questions
Because a model only reflects what it is trained and queried on. Eight companies out of ten name data as the first obstacle to scaling AI: the algorithm is rarely the missing piece, the prepared, governed and reliable dataset is.
With one use case rather than an exhaustive audit. A real use case reveals what the foundation is missing faster than a survey does, and it gives the programme a figure to defend. The rest of the journey is then built out from what it exposed.
No, and waiting for a perfect foundation is the surest way to deliver nothing. Governance covers the scope of the first use case, then widens as other domains connect to it. What must not be deferred is the ownership of each domain.
Yes. The foundation is built alongside the existing systems and connects them one at a time, each flow validated on its own before the next. Replacing everything up front turns a data programme into a migration project, with the risk that goes with it.
By measuring before, not after. The framing sets a baseline on the figures the business already argues about, delay, rework, duplicates, and the same measurement is repeated once the foundation is live. Without that baseline, any gain is a matter of opinion.
Not necessarily. They describe a chain, not a mandatory programme: each one is an offer of its own. What does not change is the order of dependency, forecasting on ungoverned data learns the defects of that data rather than the business signal.
Framing takes 4 to 6 weeks depending on the number of sources, entities and the level of compliance required, and produces a quality audit, a value and feasibility matrix and a prioritised roadmap. A usable foundation with a first use case in a real environment follows in 6 to 10 weeks, with a go or no-go before scaling.