Hochpraezise digitale Zwillinge in Echtzeit

TwinPath AI entwickelt digitale Zwillinge fuer Staedte und kritische Infrastruktur

Wir transformieren komplexe Anlagen in operative 3D-Umgebungen mit Live-Daten und KI, damit Teams Entscheidungen vor und waehrend realer Einsaetze sehen, simulieren, antizipieren und optimieren koennen.

Mit dem Team sprechen Loesungen ansehen

Operativer Leitstand

Echtzeit-Transparenz fuer Betrieb, Risiko und Planung.

3D + data + AI

High-fidelity twins for simulation, prediction and validation.

TwinPath AI digital control tower visual

Digitale Zwillinge in Echtzeit fuer sicherere, intelligentere und effizientere Ablaufe

Uber TwinPath AI

Was TwinPath AI macht

TwinPath AI builds and operates real-time, high-fidelity digital twins for cities and safety-critical infrastructure. We combine precise scanning, GIS, point clouds, field measurements, operational data and AI models in a 3D environment where owners and operators can understand asset condition, test scenarios and optimize decisions.

Wir sind ein oesterreichisch-rumaenisches Start-up und verbinden Engineering-Expertise aus Oesterreich und Rumaenien, um verlaessliche Digital-Twin-Systeme fuer oeffentliche und industrielle Betreiber zu liefern.

Advanced digitization GIS capture, on-site measurements and point cloud collection with high precision.
Live integration Connection to sensors, operational systems and real-time data for continuous awareness.
AI & simulation Simulations, anomaly detection, predictive maintenance and measurable KPI optimization.

Unsere Mission

To turn complex assets into digital decision tools so operators can reduce risk, improve resilience and invest more intelligently.

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Loesungen

Drei Kernrichtungen von TwinPath AI

We build digital platforms that unite 3D representation, operational data and AI to deliver control, prediction and simulation at system level.

Prozess ansehen

1. Control tower digital twin for operations and planning

  • Real-time situational awareness for traffic, utilities, services and infrastructure.
  • AI-powered optimization for traffic, energy, maintenance, logistics and workforce routing.
  • Decision sandbox to test interventions and select the best ROI / risk option.

2. Digitalization layer for critical infrastructure

  • Single source of truth from design and construction to operations.
  • Reduced change orders, compliance validation and improved uptime.
  • Preparation for incidents and resilience against physical, cyber and climate-related risks.

3. Simulation factory for autonomy and robotics

  • Realistic 3D platform with sensor and physics simulation.
  • Dynamic scenarios and synthetic data with automated labeling for model training.
  • Sim2real workflows for safer validation and faster time to market.

4. Virtuelles Design und Validierung fuer militaerische Mobilitaet

  • Qualitaetsnachweis-Workflows fuer zivile Behoerden vor dem Feldeinsatz.
  • Sicheres Testen gefaehrlicher Szenarien in der Simulation, inklusive extremer und seltener Bedingungen.
  • Kuerzere Entwicklungszeit und niedrigere Kosten durch weniger physische Prototypen und Feldtests.
Vorgehen

From data capture to operational optimization

1. Capture GIS, on-site measurements and point cloud collection for a faithful asset model.
2. Build We build the high-fidelity 3D twin in an Unreal-based environment.
3. Connect We connect the twin to live data, sensors and operational flows for real-time awareness.
4. Simulate & Optimize We run simulations, AI analytics, anomaly detection and predictive maintenance for better decisions.

Target outcomes

  • Reduced operational and investment risk.
  • Higher uptime and better operational efficiency.
  • Validation of interventions before execution.
  • Improved ROI through data-driven planning.

Key capabilities

  • Stress testing for operational and emergency scenarios.
  • Anomaly detection and fault prediction.
  • Optimization for capacity, traffic, energy and maintenance.
  • Validation for autonomy, robotics and synthetic data pipelines.

Validierungsmodule aus Projekten zur militaerischen Mobilitaet

Digital Vehicle Twin Methodik Integration eines Fahrdynamikmodells, Aufbau eines 3D-Geometriemodells, Erfassung realer Manoeverdaten und Korrelation von Modell und Messung fuer laterale, longitudinale und vertikale Dynamik.
Digital Environment Twin Pipeline LiDAR/Camera/GIS Scan-to-Twin Workflow in Unreal Engine mit 1-2 cm Genauigkeit sowie Materialien, HD-Texturen, 3D-Objekte und dynamische Effekte (Sonne, Schatten, Wetter).
Weitere Services und KPI Szenario-Generierung und Integration sowie SLAM-Performance-Evaluierung mit Absolute Trajectory Error, Relative Pose Error, Loop Closure Detection und Echtzeit-Verarbeitungsgeschwindigkeit.
Branchen

Wo TwinPath AI Mehrwert schafft

The platform is designed for complex environments where real-time visibility, simulation and risk reduction are essential.

Airports

Operational flows, safety, capacity planning and resilience.

Ports

Port logistics, traffic, maintenance and critical infrastructure monitoring.

Factories

Predictive maintenance, operational optimization and process simulation.

Cities

Urban operations, infrastructure, mobility, services and strategic planning.

Verteidigung und militaerische Mobilitaet

Virtuelle Validierung fuer autonome und missionskritische Systeme mit sichererer Testabdeckung und schnellerer Einsatzreife.

Team

Team

Das TwinPath AI Leadership-Team:

Mihai Nica

Mihai Nica

CEO und Gruender

Mihai verantwortet Strategie und Produktausfuehrung bei TwinPath AI mit Fokus auf operative digitale Zwillinge fuer Staedte und kritische Infrastruktur.

mihai.nica@twinpathai.ro LinkedIn
Franz Wotawa

Franz Wotawa

Co-Founder

Franz Wotawa received a M.Sc. in Computer Science (1994) and a PhD (1996) from the Vienna University of Technology. He is Professor of Software Engineering at Graz University of Technology and has authored more than 390 peer-reviewed publications. His work spans model-based reasoning, verification and validation, software testing and debugging, with strong focus on bridging research and industrial practice.

franz.wotawa@twinpathai.ro LinkedIn
Andrei Vilcea

Andrei Vilcea

CDO

Andrei steuert die Datenstrategie von TwinPath AI und koordiniert Datenqualitaet, Governance und Decision-Intelligence-Faehigkeiten.

andrei.vilcea@twinpathai.ro LinkedIn
Kontakt

Lassen Sie uns den naechsten digitalen Zwilling bauen

Wenn Sie digitale Zwillinge fuer Staedte, kritische Infrastruktur, operative Simulation oder Robotics-Validierung besprechen moechten, kontaktieren Sie uns direkt per E-Mail.

office@twinpathai.ro
Buero Bukarest Splaiul Independentei 294E, Bucuresti Sector 6, Romania
Buero Graz Inffeldgasse 16b, 8010 Graz, Austria
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Digitale Zwillinge in Echtzeit fuer sicherere, intelligentere und effizientere Ablaufe