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Symphony Industrial AI

Digital Manufacturing Division

Letter from the President

It’s an exciting time to be part of Symphony Industrial AI. At the Digital Manufacturing division, our focus is on delivering AI-based digital solutions with our clients that empower human-machine teams to drive more productive, safe, and sustainable manufacturing operations. AI has barely penetrated the manufacturing operations space and we intend to lead.

Our clients are global leaders within their industries, and we help them solve some of their most complex digital manufacturing challenges. We consult with our clients and together develop solutions which deliver high-impact business value leveraging our technology. When we do this, we become a trusted advisor.

Our culture is dynamic and flexible. Our people have a passion for customers and technology and are excited to be a part of a fast-paced, high-growth, entrepreneurial culture – that’s who we are.

— Barry Johnson

SIAI Digital Manufacturing History

Savigent acquired by Symphony IndustrialAI

Next Milestone

2021

Install base grows to over 500 global sites

Next Milestone

2019

Patent granted for Patterned-Based Service Bus using Activity Oriented Services

Next Milestone

2016

Patent granted for Workflow Management in a Distributed System

Next Milestone

2014

Patent granted for Agent Based Development and Runtime

Next Milestone

2012

Company name is changed to Savigent

Next Milestone

2011

Workflow product developed and deployed

Next Milestone

2010

MES Client developed

Next Milestone

2007

Seagate Technology becomes a customer

Next Milestone

2003

First software product is launched - IQB

for installation in several Ford automotive factories

Next Milestone

1998

Nash Controlware is founded by Joel Nash and Steve Barnett

1994

Plant Performance Division

Symphony IndustrialAI plant performance applications span asset predictive maintenance and process health and optimization, maintaining high availability of equipment, extending the life of capital assets, and reducing process variability.

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