Building digital twins for manufacturing just got easier

Introducing the first TwinOps platform for process engineers to
physics-informed AI.
tech transfer
scale-up
continuous validation
real-time release
cGMP manufacturing
Introducing the first TwinOps platform for process engineers to

physics-informed AI
tech transfer
scale-up
continuous validation
real-time release
cGMP manufacturing

Building digital twins
for manufacturing just got easier

See our Platform
Challenge

Operationalizing AI can unblock today’s manufacturing bottlenecks

Process Engineers want to improve productivity, quality and efficiency. However, they lack the tools to do so. 

Today’s ML platforms do not solve engineering problems, and most ML projects are piloted but lack the technology scalability to be operationalized.

As a result, process knowledge is locked away in spreadsheets and the minds of engineers.  

Process Engineers want to improve productivity, quality and efficiency. However, they lack the tools to do so. 

Today’s ML platforms do not solve engineering problems, and most ML projects are piloted but lack the technology scalability to be operationalized.

As a result, process knowledge is locked away in spreadsheets and the minds of engineers.  

basetwo home platform
Platform Feature Overview

With a no code, drag-and-drop interface, basetwo allows users to:

Integrate with multiple data sources and in-house spreadsheets
Clean ingested data for analysis
Easily build and validate advanced ML and process models
Deploy and monitor models to enhance manufacturing SOPs
Collaborate with performance reports across departments
Design, test and deploy process optimization strategies
Run scenarios with digital twins using plant or lab data
Validate process or equipment performance
Build and scale internal data science competency
Expertise

Unlock deeper process understanding

Basetwo uses physics-informed machine learning that integrates process engineering with data science. This allows engineers to leverage the speed and scalability of machine learning while still learning the underlying dynamics.
 
Unlock deeper process understanding
Basetwo uses physics-informed machine learning that integrates process engineering with data science. This allows engineers to leverage the speed and scalability of machine learning while still learning the underlying dynamics.
Operationalize AI
Operationalize AI

The success or failure of AI springs from the people who create, use, and maintain them

From operators who steer processes, to process engineers who use domain expertise to drive efficiency, to managers who track performance and budgets.

A people-oriented, integrative platform can supercharge productivity and performance when individuals, digital technologies, and advanced analytics work together.

From operators who steer processes, to process engineers who use domain expertise to drive efficiency, to managers who track performance and budgets.

A people-oriented, integrative platform can supercharge productivity and performance when individuals, digital technologies, and advanced analytics work together.

See how isomer.ai enables data-driven outcomes for:
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MS&T Directors
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Process Engineers
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Process Dev. Scientists
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Process Dev. Scientists
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