Expertise III
Useful AI starts with the data you already have.
Key figures
We are not an AI laboratory. We are a data company: we train and put models into service from your existing systems — ERP, data warehouse, integration flows — and we deliver them where your teams make decisions, in their dashboards. An AI project is first a data project: prepare the data, identify the use cases, prototype, industrialise, govern and measure.
The right question is not “which model?” but “which business decision does this model allow us to take a week earlier?”
ERP, DWH, integration flows, reporting: this is our everyday work. A model is only as good as the flows that feed it — the part most AI projects underestimate.
We do not deliver a notebook: the prediction becomes a fact table integrated into your data model, displayed in the dashboard your teams already open.
Training traceability, continuous evaluation, hardened and monitored infrastructure. A model that is neither measured nor monitored degrades silently.
From source data to dashboard: each step produces a verifiable deliverable.
Source tables, population split, business exclusion filters, explanatory variables, missing values, encoding.
Deliverable: documented training and test sets.
Several possible targets for the same need, comparative training of several families of algorithms, measurement on the test set, reasoned selection.
Deliverable: quantified comparison, selected model and its limitations.
Application of the model to the population not yet realised, generation of a fact table attached to the existing data model, periodic re-runs.
Deliverable: prediction table integrated into the DWH.
Dashboard comparing the actual indicator and the predicted indicator on the same time axis, with business filters and configurable tolerance thresholds.
Deliverable: operational dashboard and documented gaps.
This is the method of a predictive project; it sits within the DataReady approach that frames the engagement as a whole.
Each offer starts from a concrete business need, describes what we put in place and rests on a project delivered by our teams.
The need. The on-time delivery rate is only known once the delay has been suffered.
Our offer. Predictive model on the order history, delivered next to the actual indicator.
Our proof. Predictive model measured on 5.6 million lines. Read the case study
The need. The cash forecast rests on theoretical due dates that customers do not keep.
Our offer. Invoice-by-invoice payment date prediction, delivered in the customer accounts report.
Our proof. Model delivered on a receivables ledger consolidated from several ERPs. Read the case study
The need. Customers and suppliers handled with the same rules although their behaviours differ.
Our offer. RFM clustering (recency, frequency, monetary value) producing a score usable as a steering variable.
Our proof. Segmentation delivered and fed back into a lead-time predictive model.
The need. Standards, specifications, quality procedures: classic search does not surface the right information.
Our offer. Production RAG pipeline: embeddings, hybrid search, reranking, sourced answers.
Our proof. Sourced answers, measured factual accuracy.
The need. Useful data is locked in PDFs (machine reports, supplier documents) and re-keyed by hand.
Our offer. Automated document reading, structured extraction and direct feeding of business calculations.
Our proof. Manual re-keying eliminated on an industrial costing process.
The need. Compliance errors in a regulatory file are discovered after submission.
Our offer. Conversion to the target format and automatic validation against the official schemas before any submission.
Our proof. Errors detected before submission; product industrialised and deployed.
Forecast by part number and by site, from stock movements and production orders. To be scoped with you: a proposed approach, not an existing achievement.
Conversational agent orchestrating actions in your business applications. Assistant in service in a company.
Execution tracing, evaluation loops, structured outputs, fallback between models. In place on our production agents.
Shared architecture, caching, load balancing, arbitration between models. Inference costs reduced in a measured way through pooling and caching.
You do not have to commit to a full programme to get started: each step is decided in light of the previous one. Indicative durations, to be adjusted to the actual scope.
Qualification of the expected business value, inventory and audit of the available data, definition of the target indicator and the exclusion rules, feasibility estimate and quantified success criterion.
Output: scoping note and a reasoned decision to launch — or not — a POC.
Data preparation, comparative training of several models, generation of predictions on the target population, reporting dashboard and presentation of the measured results to the business sponsor.
Output: measured model, operational dashboard, costing of the industrialisation. This step is available as a packaged offer: AI proof of concept in 8 weeks.
Integration of the retraining and prediction flows into the scheduler, production release of the model and the dashboard, quality monitoring, skills transfer to the internal teams.
Output: model in service, monitored, documented and maintained.
Use case scoping is also the AI component of the Data & AI Assessment; it can be run on its own.
Upskilling your teams in generative AI is covered by a dedicated offer: Train your teams in generative AI.
Management in France covering governance, trade-offs and the client relationship; a nearshore service centre in Casablanca for delivery, with an adjusted daily rate and continuity of service. Weekly operational review and monthly steering committee.
An AI project handles sensitive data: order book, costs, production data, technical documentation. Our processing environments are hardened according to ISO 27001 standards:
Supply chain · International industrial equipment manufacturer
Turning seven years of orders accumulated in the Operations datamart into a forecast of On Time Delivery for the months ahead, delivered in the dashboard the teams already use.
Artificial Intelligence & Automation · BI & Data Analytics
Read the case studyFinance · Finance department of an industrial group
Replacing theoretical due dates with the actual payment behaviour of each customer account, and delivering the forecast in the report the finance team already opens.
Artificial Intelligence & Automation · BI & Data Analytics
Read the case study
Reference data, quality, catalogue and business rules: reliable, controlled data shared across the organisation.
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Data warehousing, modelling and reporting: a 360° view of your business and your customers, built on solid foundations.
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Migration and modernisation of your data platforms, reliably, securely and flexibly, while keeping control of cost.
Learn moreQuantified results correspond to achievements measured on the scopes and datasets described; they do not constitute a performance commitment on any other scope. Offers flagged as "to be scoped" describe a proposed approach, not an existing achievement.
A half-day workshop to select the use case with the highest value, then a costed scoping note before any delivery commitment.
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