Basetwo
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About
Basetwo is an AI-driven platform specifically engineered for the manufacturing sector, focusing on high-stakes industries like pharmaceuticals, specialty chemicals, and consumer packaged goods. Its primary purpose is to help companies modernize their production processes by transitioning from traditional, time-consuming physical experimentation to model-driven virtual simulations. By centralizing data from various plant and lab sources, the platform enables manufacturers to create a unified environment for their operations, ultimately aiming to accelerate time-to-market and significantly improve operational resilience. The platform works by building explainable digital twins—mathematical representations of physical manufacturing processes that remain interpretable for human engineers and regulatory bodies. Unlike black box AI, Basetwo provides transparent insights into why certain recommendations are made. The workflow involves connecting disparate databases, such as OSI-PI or lab files, performing data preparation, and then deploying models that offer real-time recommendations. These recommendations focus on optimizing setpoints to minimize raw material waste, lower energy consumption, and shorten cycle times while maintaining high product quality. Basetwo is best suited for cross-functional manufacturing teams, including Manufacturing Directors, Process Engineers, and Modelers. It serves a wide range of use cases, from early-stage process development and scale-up to commercial-scale production and quality monitoring. Whether a team is looking to scale a new biologic drug or optimize the production of specialty coatings, the platform provides the analytical depth required to handle complex chemical and biological interactions. Its ability to predict deviations through real-time soft sensors makes it a critical tool for quality assurance departments. What distinguishes Basetwo from generic data science tools is its deep integration of engineering principles and its focus on industrial-scale, science-based manufacturing. While many platforms offer predictive analytics, Basetwo’s emphasis on explainability and industry-specific applications—like biologics or gas refineries—ensures that its outputs are actionable within regulated environments. The reported outcomes, such as an 80% reduction in deviations and a 50% improvement in scale-up speed, demonstrate a specialized focus on the unique pain points of large-scale manufacturing that general-purpose AI often misses.
Pros & Cons
Reduces time-to-market by up to 50% by replacing physical experiments with virtual digital twins.
Provides significant cost savings with reported 20-40% reductions in cycle times and material usage.
Improves quality control with a demonstrated 80% reduction in production deviations using soft sensors.
Uses explainable AI models, making them suitable for regulatory review in pharma and chemical industries.
Trusted by industry leaders, including 5 of the top 10 global pharmaceutical companies.
Pricing is not publicly disclosed and requires booking a demo for all potential customers.
Implementation requires technical integration with existing plant and lab databases.
Functionality is highly specialized for complex manufacturing, which may be overkill for simple operations.
Use Cases
Manufacturing Directors can use AI-driven insights to identify plant inefficiencies and hit target KPIs across the production line.
Process Engineers can create digital twins to simulate complex interactions, allowing for optimized scale-up from lab to commercial production.
Process Modelers can develop and share custom models of existing workflows to enhance collaboration and speed up development.
Quality Control Teams can implement real-time soft sensors to predict and prevent deviations before they impact product quality.
Platform
Features
• predictive recommendations
• multi-source data integration
• collaborative model development
• kpi tracking dashboard
• process setpoint optimization
• virtual scale-up modeling
• real-time soft sensors
• explainable digital twins
FAQs
Which industries is Basetwo best suited for?
Basetwo is specifically designed for complex manufacturing sectors including pharmaceuticals, specialty chemicals, and consumer packaged goods. It supports diverse production types ranging from small molecule drugs to biologics and gas refineries.
What kind of data can be connected to the platform?
Users can join various plant and lab databases, including historian data like OSI-PI and various lab-based files. The platform provides integrated tools for data preparation and joining to ensure a clean foundation for modeling.
How does Basetwo help with quality control?
The platform utilizes real-time soft sensors to predict deviations in quality before they actually occur. This allows teams to implement corrective actions immediately, which has been shown to reduce production deviations by up to 80%.
Can the AI models be used for regulatory compliance?
Yes, Basetwo focuses on building explainable digital twins that are interpretable by both engineers and regulatory bodies. This transparency is essential for maintaining compliance in highly regulated industries like pharmaceutical manufacturing.
What kind of operational improvements can be expected?
Manufacturers typically see a 20-40% reduction in manufacturing costs and cycle times. Additionally, the platform can help reduce energy and material usage by over 40% through AI-optimized process setpoints.
Pricing Plans
Enterprise
Unknown Price• Connect plant and lab databases
• Explainable digital twin building
• Real-time process recommendations
• Soft sensing for quality control
• Virtual scale-up experiments
• KPI tracking and optimization
• Custom-built shareable models
• Predictive maintenance insights
Job Opportunities
Life Sciences Enterprise Account Executive
Optimize manufacturing efficiency and reduce production costs using AI-powered digital twins and predictive recommendations for pharmaceutical and chemical teams.
Experience Requirements:
Quota-carrying Account Executive role (full sales cycle, not BDR/SDR/CS)
Experience selling into life sciences (pharma, biotech, or medtech)
Other Requirements:
Legally entitled to work for any employer in the US
Experience selling directly into Product Development, MSAT, or manufacturing/operations teams in pharma or biotech
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