Forecasting
Demand, stock and flows get planned on signals, not on gut feeling.
MACHINE LEARNING
Machine Learning
that holds up in production
A predictive model needs data, business criteria and an operating framework. We design the models, integrations and verification needed to evaluate them in your context.
What the models deliver
Detection
Fraud, breakdowns and quality drift surface before they cost you.
Scoring
At-risk customers show up early enough to act.
Extraction
Documents read themselves: the useful information arrives structured.
Reliability
Supervision watches every model and triggers retraining when it drifts.
Our Approach
A successful ML project goes far beyond training a model. Method matters as much as the algorithm.
- 01
Audit & Scoping
Analysis of your existing data and exploitable patterns. Success metrics get defined with your business teams, not in a lab.
- 02
Proof of Concept (POC)
Several algorithmic approaches compete on your real data. The best architecture wins, with evidence.
- 03
MVP & Industrialization
Complete data pipeline, feature engineering, MLOps infrastructure. Integration with your existing systems through APIs.
- 04
Production & Optimization
Performance supervision, drift detection, automatic retraining. New versions get compared before replacing the old ones.
Deliverables
- 01Trained and optimized ML models
- 02Automated data pipeline
- 03Feature store and data versioning
- 04Documented prediction APIs
- 05Performance supervision (MLflow, Weights & Biases)
- 06Automatic retraining scripts
- 07Performance and interpretability report
- 08Data Science team training
Key Technologies
PyTorchJAXTransformersvLLMTensorRT-LLMTriton Inference ServerRayLangGraphLlamaIndexHugging FaceBigQuerySnowflakeDatabricksSparkFlinkKafkadbtAirflowIcebergDelta Lake
Frequently Asked Questions
NeuroVista, Paris
Your data is one step ahead
Let's see what your history can already predict.
Scope your ML project