We're thrilled to be backing Basetwo, a no-code AI platform to help manufacturing engineers troubleshoot and optimize their production processes to increase efficiency and reduce waste. Without writing a single line of code, manufacturing engineers can use the platform to ingest, merge and transform large datasets to build their plant's digital twins. Using Basetwo's library of digital twins, engineers can improve their understanding of their plant performance and run real-time scenario analyses to improve their plant's yield, product quality, and efficiency.

Today's AI software lacks the ability to solve process engineering inefficiencies. These shortcomings include the inability to:

• Simulate manufacturing processes accurately
• Integrate with current enterprise systems
• Solve control and optimization problems
• And, to explain the process to engineers

For this reason, only 20% of other AI interventions succeed past the pilot phase.

Core features

• Data integration - Basetwo integrates existing engineering ecosystems. This benefits users by enabling them to send live KPIs to existing dashboards for visualizations, operationalize events and alerts across the workforce, and send work orders to existing maintenance management systems
• Descriptive analytics - Once all sources of industrial data are integrated into the platform, engineers can run descriptive analytics workflows on each set. These include: statistics, visualizations, plots, heat plots, correlation matrices, principal component analysis (to understand what the most influential variables are) and more. Users can also view a tabular version of their datasets through the data transformation library, where they can also transform one into another
• Simulation models - This layer to the Basetwo platform allows engineers to apply pre-built models or customized models to their processes. This allows manufacturers to incorporate equations that are specific to the processes they’re working with into their models, such as bioreactors. Each model created will be available to other users in an online library
• Pipeline explorer - This part of the platform allows engineers to run scenario analyses. Such analyses can help users better understand consequences and effects of changing certain variables in processes. For example, understanding how yield is impacted by a specific change to a solution’s pH. This feature can also calculate optimal operating points for processes given chosen constraints. You can export or visualize your results of these simulations.

The company's mission is to empower manufacturers to improve quality, transparency, and performance with industrial-grade AI by unlocking the full potential of manufacturing data. Its competitive advantage is integrating machine learning with process models that enable engineers and scientists to go beyond traditional DoEs, or Design of Experiments, by capturing the underlying dynamics driving process performance.

The team behind Basetwo

“We're excited to be investing in such a strong founding team for a second time and look forward to supporting the execution of their growth strategy," said Matthew Claxton, Principal of Sustainability at Plug and Play.

The team behind Basetwo has spent the last decade deploying AI in the heavy industry and manufacturing sectors. They’ve been across the globe and learned firsthand from engineers, operators, managers, and scientists that influence the manufacturing process. Plug and Play met the Gaffoor brothers through their previous venture, Emagin AI which was later acquired by Innovyze.

Basetwo’s strategic markets include the chemicals manufacturing, machine learning, biopharma and food manufacturing industries. Their total target market size is worth $108b. We're glad to invest in Basetwo alongside Glasswing Ventures, Argon Ventures, Graphite Ventures, MaRs IAF, Pareto Holdings, Quiet Capital, and Caffeinated Capital