GATECH S.A.GATECH S.A.
RAG & Automation

AI & Machine Learning Automation

Build intelligent data systems with machine learning, automated pipelines and predictive analytics

Python◆PyTorch◆TensorFlow◆Apache Spark◆Airflow◆Kubernetes◆Vector Databases◆LLM APIs◆Python◆PyTorch◆TensorFlow◆Apache Spark◆Airflow◆Kubernetes◆Vector Databases◆LLM APIs◆Python◆PyTorch◆TensorFlow◆Apache Spark◆Airflow◆Kubernetes◆Vector Databases◆LLM APIs◆

Machine Learning Models

Custom ML model development tailored to your business data and specific prediction or classification needs.

Automated Data Pipelines

Scalable pipelines for data ingestion, transformation, feature engineering and machine learning model deployment.

Predictive Analytics

Forecast future trends, demand, and risks using historical data and advanced statistical models.

NLP and Document Intelligence

Natural language processing for automated document analysis, extraction, classification and semantic search at scale.

MLOps and Model Deployment

Infrastructure and workflows for versioning, monitoring and continuous deployment of machine learning models.

RAG Assistants & Semantic Search

RAG-powered agents and assistants that connect your company data to language models — answering questions with cited sources, with nothing sent to external APIs.

Data Architecture

How your data flows

Data Sources

Data Ingestion

Feature Engineering

ML Models

Prediction API

Dashboards / Automation

Real-time predictionsScalable data pipelinesAutomated decision systems
Trusted by
0%+

average reduction in manual processing time

<2 wks

time to deploy first production model

0.0%

data pipeline uptime

Use Cases

Real-world deployments

Fraud detection

Fraud detection

Demand forecasting

Demand forecasting

Customer behavior prediction

Customer behavior prediction

Document automation

Document automation

Operational optimization

Operational optimization

Why choose us?

We combine deep ML expertise with practical business knowledge to deliver AI solutions that create measurable value, not just impressive demos.

STEP 1
Data Discovery
Audit of data sources and definition of business goal
STEP 2
Model Prototyping
Feature engineering, model training and validation
STEP 3
Production Deployment
MLOps, prediction API, systems integration
STEP 4
Monitoring & Refinement
Model drift monitoring and periodic retraining