Getvia provides an AI Copilot for semiconductor manufacturers that continuously analyzes real‑time and historical sensor, telemetry, and operational data to predict equipment failures and recommend optimal maintenance actions. Leveraging a multi‑agent architecture with retrieval‑augmented generation, graph mapping, and digital twin integration, the platform delivers instant, context‑aware insights that reduce unplanned downtime, extend asset life, and improve safety and compliance reporting.
Funding
Funding not disclosed
Founders
Product
Problem
Semiconductor manufacturers often experience unexpected equipment failures and inefficient maintenance schedules, leading to costly unplanned downtime, reduced yield, and safety compliance challenges. Existing monitoring tools lack real-time, context-aware analysis of the vast sensor and operational data needed to predict failures accurately.
Solution
Getvia’s AI Copilot leverages agentic AI to continuously analyze thousands of real-time and historical data points from semiconductor production lines. Specialized agents retrieve and interpret information from manuals, SOPs, fault logs, and MES systems, while graph and database agents map equipment relationships to provide contextual insights. Integrated with digital twin models, the platform profiles operating conditions, maintenance history, and sensor anomalies to forecast failures and recommend optimal service schedules. The system delivers instant, actionable recommendations to operators and maintenance teams, reducing unplanned downtime, extending asset life, and improving safety and compliance reporting across the factory floor.
Target Audience
Primary customers are semiconductor fab operators, maintenance managers, and engineering teams seeking to improve equipment reliability and production efficiency in high‑mix, high‑volume manufacturing environments.
Features
- Multi‑agent AI architecture that combines Retrieval‑Augmented Generation (RAG), graph mapping, and database querying for comprehensive equipment context
- Digital twin integration that continuously updates a virtual replica of assets with sensor data, maintenance records, and operational parameters
- Real‑time analytics of telemetry, ERP work orders, BOM, and PLM change orders to generate predictive maintenance alerts
- Automated recommendation engine that suggests optimal maintenance actions and schedules to minimize downtime
- Instant access to equipment, production, quality, and safety information through a unified dashboard
- Seamless integration with existing MES, ERP, and PLM systems via APIs and data connectors