Our approach
We don't ship POCs. Every project is structured from the start to reach production: deployment architecture, IS integration, MLOps and monitoring defined during the assessment phase, not as an afterthought.
Method
Each step has a clear deliverable. We only move to the next one when the validation criteria are met: not on a schedule commitment.
01
1 to 2 weeks
Use-case qualification, inventory of available data, analysis of the existing IS, deployment constraints. Result: a technical specification and a ROI estimate.
02
3 to 6 weeks
Definition of the technical stack, data collection and preparation, first working model on real data. The prototype is tested on the scope defined during assessment: not on synthetic data.
03
4 to 8 weeks
Development of the complete system, connection to equipment and IS (OPC-UA, REST, ERP), operator interface, load and regression testing. Validation under conditions close to production.
04
2 to 4 weeks
Go-live on the target scope, progressive rollout, validation of performance and availability SLAs. Skills transfer to the maintenance and operations teams.
05
Ongoing
Monitoring of input distributions and performance metrics, drift detection, scheduled retraining on new data, continuous improvement. The system improves with real production data.
Principles
The deployment architecture, SLAs and integration constraints are defined during the assessment phase. We don't discover production constraints at the end of the project.
The prototype is trained and validated on your real production data. A model that works on synthetic data guarantees nothing on the line.
Each step has objective pass criteria defined in advance: detection rate, MAPE, latency, false-positive rate. No progress on good faith.
OPC-UA, Profinet, ERP or CMMS integration is planned in the architecture, not added at the end of the project. Network and industrial security constraints are known from the start.
Monitoring, versioning and a retraining pipeline are delivered with the system. A model without monitoring is not a production system.
Technical documentation, training of the maintenance and operations teams, runbook. The goal is for you to be able to evolve the system without depending on us.
The team
Computer vision, time series, industrial NLP, MLOps, edge deployment. Specialized profiles: not generalists who pivot from one project to the next.
What we don't do
No demo without real data
We don't build POCs on demonstration data. If the data isn't available, we start with an audit of the sources.
No model delivery without infrastructure
A .pkl file delivered without a serving, monitoring and retraining pipeline is not a production deliverable.
No results commitment without qualified data
The orders of magnitude of results are conditional on the quality and representativeness of the data. We say so during assessment.
Get started
A 30-minute technical exchange to qualify the case, identify the available data and estimate the scope of the first deliverable.