Wiremind Tech Stack

AI-powered revenue optimization for transportation, cargo, and live events

Software Development Paris, Île-de-France 51–200 employees Founded 2014 Privately Held

Wiremind operates a machine-learning platform for revenue and capacity optimization across airlines, railways, buses, and sports venues. The tech stack is heavily ML-focused—PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM—paired with data infrastructure (Druid, ClickHouse, PostgreSQL) and orchestration (Argo, Kubernetes). Hiring is concentrated in data roles (7 positions) with a significant intern cohort, signaling rapid model development and engineering momentum. Active projects span 3D cargo optimization, ticket pricing, and demand forecasting—matching documented pain points in capacity planning and demand prediction.

Tech Stack 42 technologies

Core StackPython Kubernetes ClickHouse PostgreSQL PyTorch scikit-learn pandas NumPy MLflow GitLab Prometheus Grafana TensorFlow Pandas Linux Jira Flask FastAPI Argo Workflows Druid Gymnasium Kibana SQLAlchemy Keras LightGBM XGBoost Dask Dash Jupyter Bash+12 more

What Wiremind Is Building

◆Challenges

Capacity optimization
Ticket forecasting
3d optimization of air freight
Optimizing client capacity
Building security culture
Protecting infrastructure
Improving cargo demand forecasting
Reducing data drift
Improving documentation clarity
Reducing reliance on customer success

▲Active Projects

Revenue management solution cayzn
3d optimization of air freight
Ticket forecasting and pricing
Scraping competitor prices
Analytics and ab testing tools
Soc monitoring
Vulnerability management
Penetration testing
Cargostack
Improving current models in production

Hiring Activity

Accelerating15 roles · 8 in 30d

Department

Data

Product

Engineering

Design

Finance

Security

Seniority

Intern

Mid

Lead

Director

Junior

Senior

Notable leadership hires: Head of Machine Learning Operations

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About Wiremind

Wiremind builds AI-driven optimization solutions for high-value, data-intensive industries: passenger transport (railways, buses), air cargo, and live entertainment (sports, events). Founded in 2014 and based in Paris, the company serves industry leaders in France and internationally. Their platform ingests operational data—passenger flows, cargo manifests, ticket demand—and applies machine learning (revenue management, yield optimization, capacity planning) to improve pricing, inventory allocation, and utilization. The product combines predictive models with operational tooling: forecasting engines, pricing engines, and analytics dashboards. Teams handle both model development (improving production performance, reducing data drift) and infrastructure (Kubernetes-based deployment, monitoring via Prometheus and Grafana).

HeadquartersParis, Île-de-France

Company Size51–200 employees

Founded2014

Hiring MarketsFrance

Frequently Asked Questions

What machine learning frameworks does Wiremind use?

PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, and LightGBM. Supporting tools include MLflow, pandas, NumPy, and Gymnasium for model training and orchestration.

What industries does Wiremind serve?

Passenger transportation (railways, buses), air cargo and freight, and sports & events (ticket sales optimization). Clients are industry leaders in France and abroad.