Factory Builder AI

Practical Impact: Factory Builder AI has successfully developed and validated an MVP that integrates AI, digital twins, and simulation for factory layout optimization

• MVP Developed: AI-powered platform for factory layout optimization and digital twin simulation. Validated in 7+ pilot projects across Kazakhstan, Kyrgyzstan, Germany, Korea, and the USA.
• Simulation Model: Multi-level industrial simulation (product → cell → full factory) with AI-based flow and efficiency prediction.
• Pilots conducted with Severstal, OKAN, KandaSoft, TimelySoft, Matkasymov LLC, Gazprom Neft, FabLab Bishkek, Kyrgyz National University, ITMO University, Ilmenau Technical University, and Kyrgyz State Technical University.
• The pilots demonstrated up to 40% reduction in CAPEX, 25% faster decision-making, and 40% productivity improvement.

Scientific Output:

• 10+ research papers published (Scopus Q2, WoS).
• 2 registered software copyrights.

Recognition & Awards:

• Finalist – HTP × MIT DeepTech 2025.
• Winner – Best Research Work (KSTU 2023, ITMO 2023, HTP 2025).
• II Place Award – AI Science Section AI-SANA at Astana Hub Digital Bridge 2025.
• II Place Award – Industry 4.0 Pitch Battle at Digital Bridge 2025 organized by Astana Hub

TehgizShevronOil and other kazakh industrial partners provided their interest to localize the project in Kazakhstan. The team is taken a part in Industrial Acceleration 2025 program by Astana Hub.

Software Stack:

• AI & Simulation: Python (TensorFlow, PyTorch), AnyLogic, MATLAB, OpenSim
• Data & Analytics: Google Cloud Platform (BigQuery, Vertex AI), Airtable, Grafana
• Integration & Automation: REST API, OPC UA, MQTT, Modbus, CSV/JSON import
• Visualization: Blender 3D, Power BI, Unity for interactive factory mapping

Equipment & Hardware:

• Industrial PCs and PLC controllers (Siemens S7, Coolmay L02M24R) for data acquisition
• GPU workstations (RTX A5000, 64 GB RAM) for AI training and simulation rendering
• IoT sensors (temperature, vibration, flow, energy) for digital twin synchronization
• Edge computing nodes and local servers (for on-site simulation and control testing)

Auxiliary Systems:

• Cloud infrastructure on Google Cloud / AWS
• Local server cluster at R&D Lab
• GitHub-based CI/CD pipeline for model updates
• Collaboration tools: Notion, Slack, Zoom, and GitLab for team coordination

The development of Factory Builder AI required solving complex interdisciplinary challenges at the intersection of artificial intelligence, industrial engineering, and simulation modeling. Integrating AI algorithms with CAD/CAE tools and industrial communication standards (OPC UA, Modbus) demanded advanced software architecture and deep understanding of production systems. Another major difficulty was limited access to real industrial data in Central Asia, which required the creation of synthetic datasets and digital twins for model training. Achieving high accuracy in flow simulations and ROI prediction also involved extensive testing and computational resources. Despite these challenges, the team built a fully functional MVP, validated it through multiple pilots, and demonstrated measurable business impact in real industrial environments.

Summary

Factory Builder AI is an AI-powered platform for smart factory design, simulation, and optimization, developed to help industrial enterprises reduce CAPEX and OPEX by improving the efficiency of factory layout design and modernization. It enables companies to model production facilities digitally, analyze scenarios of material, energy, and personnel flow, and automatically find optimal configurations using artificial intelligence.

Problem. Globally, more than 50% of industrial projects never reach expected payback due to poor planning, outdated standards (ISA-95), and fragmented “island” automation. Enterprises in developing economies face additional challenges: lack of simulation expertise, high costs of imported software, and the absence of localized AI tools. As a result — low flexibility, high CAPEX, and reduced competitiveness.

Solution
Factory Builder AI bridges industrial needs and scientific innovation by combining AI algorithms, digital twins, and simulation models in a single platform. The system allows engineers to:

• Visually design and configure production layouts via drag-and-drop
• Run AI-driven simulations to identify bottlenecks and optimize flows
• Predict failures and evaluate ROI of modernization scenarios
• Adapt factory design dynamically to changing inputs (materials, personnel, energy)

The platform integrates seamlessly with CAD/CAE systems (SolidWorks, AutoCAD) and industrial protocols (OPC UA, Modbus), making it applicable both for greenfield and brownfield facilities.

Technology Stack. AI/ML (PyTorch, TensorFlow), Simulation (AnyLogic, MATLAB, OpenSim), CAD (SolidWorks), and Cloud infrastructure (Google Cloud, Vertex AI). The platform uses hybrid AI models combining generative design, optimization algorithms, and reinforcement learning to propose optimal factory configurations. All simulation data can be exported as digital twins for real-time monitoring and predictive maintenance.

Factory Builder AI is designed for industrial enterprises in Central Asia and emerging markets, where the adoption of Industry 4.0 technologies is still limited. The project originated in Kyrgyzstan and has expanded through collaborations in Kazakhstan, Germany, Korea, and the United States. Pilot projects have been implemented at Kyrgyz National University, Kazakhstan Engineering, and Technopark HTP (Belarus), confirming the solution’s adaptability across different industrial environments.

The next stage focuses on scaling within Kazakhstan’s industrial ecosystem, particularly in machinery, metallurgy, and energy sectors, with further plans to enter the broader CIS, Middle East, and Southeast Asian markets through strategic partnerships and regional accelerators.