Honeywell Forge

Honeywell Forge is Honeywell’s open-architecture, hardware-agnostic industrial intelligence platform for connecting installed assets, operational technology (OT), enterprise data, and control systems, then applying domain-specific models, analytics, and AI to support increasingly autonomous operations.

1. Operational intelligence: Forge is designed to work across existing infrastructure, including third-party systems, rather than requiring organizations to replace their installed equipment. It contextualizes data from physical assets using Honeywell’s domain models and bridges IT and OT information for real-time operational decision-making.
2. AI and automation: Honeywell Forge combines operational domain knowledge with deterministic AI models and real-world operating constraints. Its AI Engine provides a foundation for deploying AI across enterprise operations, while Honeywell also reports running domain-trained agentic workflows in live customer environments to automate processes and support operational decisions.
3. Industrial applications: the Forge portfolio includes applications for production intelligence and asset performance. Production Intelligence provides real-time monitoring, early anomaly detection, forecasts, guided root-cause analysis, deviation prediction, and recommended actions, while Asset Performance applies predictive analytics and hybrid AI/ML models to equipment health and reliability.
4. Deployment and commercial model: Forge consists of an underlying intelligence platform, industry applications, an AI Engine, and Honeywell digital services rather than one fixed software package. Public standard pricing is not listed, and deployment requirements vary by application; factors such as connectivity, historian and operational-data availability, existing control systems, cloud requirements, and the specific Forge applications selected affect implementation.

Honeywell Forge is most relevant to industrial and asset-intensive organizations that want to use existing OT infrastructure and operational data for production intelligence, asset reliability, and progressively more automated decision-making rather than introducing a standalone analytics dashboard.