🤖 Artificial Intelligence
AI models that learn your data
Machine learning, NLP, LLM/RAG and computer vision — models trained on your business data to predict, classify and automate your critical decisions.
data_ia_pipeline.py
$ python train_model.py --dataset sales_2024
Loading data…
Preprocessing & features…
[Training] XGBoost Classifier
Epoch 1/50 │ Loss: 0.842 │ Acc: 73.2%
Epoch 25/50 │ Loss: 0.198 │ Acc: 94.1%
Epoch 50/50 │ Loss: 0.087 │ Acc: 97.3%
97.3%
Accuracy
0.984
AUC
0.962
F1 Score
0.951
Precision
Model saved → models/sales_churn_v2.pkl
Expertise
Our AI & ML capabilities
From supervised models to autonomous LLM agents — each solution is tailored to your data and sector.
Classification & Prediction
Churn, lead scoring, fraud detection, demand forecasting — XGBoost, Random Forest, neural networks.
NLP & Text Analysis
Sentiment, entity extraction, ticket classification, auto-summarization — transformers and BERT models.
Computer Vision
Object detection, image classification, visual quality control — YOLO, ResNet and CNN architectures.
Time Series
Sales forecasting, anomalies, predictive maintenance — Prophet, LSTM, ARIMA models.
LLM & RAG
Conversational agents on your internal data, retrieval-augmented generation, LangChain and OpenAI pipelines.
Anomaly Detection
Real-time monitoring, proactive alerts, isolation forest and autoencoders to detect the unexpected.
Method
Our MLOps approach
1
Discovery
Analysis of your available data, definition of objectives and success metrics for your AI use case
2
Preparation
Cleaning, feature engineering, reproducible data pipeline and dataset versioning
3
Training
Algorithm selection, hyperparameter tuning, cross-validation — always with your business domain
4
Evaluation
Robustness tests, detailed metrics, model comparison and validation with your field teams
5
Deployment
REST API, integration with your existing tools, drift monitoring and automatic retraining
Metrics
Measurable models
Every model delivered with a complete performance report: ROC curves, confusion matrices, SHAP values for explainability.
Average accuracy97%
Average recall96%
Average AUC score98%
Inference time < 50ms100%
Model comparison
LR
DT
KNN
SVM
RF
XGB
NN
97%
Best acc.
0.98
AUC
< 50ms
Inference
✓
Explainable
Stack
Our AI technologies
Python
TensorFlow
PyTorch
Scikit-learn
LangChain
OpenAI API
Hugging Face
MLflow
Pandas
Spark ML
FastAPI
SHAP
dbt
Airflow
Docker
Results
Real use cases
E-commerce / SaaS
Customer churn prediction
XGBoost model trained on 3 years of behavioral data — 94% accuracy, proactive interventions that reduced churn by 31%.
Legal / HR
HR document NLP processing
NLP pipeline to classify and extract key clauses from 50,000 contracts — 40× faster processing than manual review.
Finance / Insurance
Real-time fraud detection
Anomaly model detecting 97% of fraud with less than 0.1% false positives — integrated into transaction API in < 30ms.