Applied Computing, a London-based startup developing an AI foundation model for the oil, gas and petrochemical industry, has raised $20 million in Series A funding. The round was led by engineering giant KBR, with participation from Databricks Ventures.
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| Credit: Schmooly / Applied Computing |
Applied Computing targets a fragmented industrial data problem
Industrial facilities already generate enormous volumes of operational data. However, bringing that information together remains a major challenge for energy companies.
According to Applied Computing co-founder and CEO Callum Adamson, operators make decisions using less than 8% of the data available to them. Much of the information is already being collected, but it remains difficult to combine data from different sources into a single, usable picture.
That data can include sensor readings, engineering documentation, and information related to physics and chemistry. The challenge is not simply collecting more data, but connecting the different types of information so operators can use them together.
Applied Computing is developing its AI model around this problem, with the aim of creating a foundation model for entire oil, gas and petrochemical facilities.
Startup founded in 2023 raises $20 million Series A
Applied Computing was founded in 2023 and is based in London. Its technology is focused specifically on industrial environments across the oil, gas, refining and petrochemical sectors.
The company has now raised $20 million in Series A funding, led by KBR. Databricks Ventures also participated in the round.
The investment comes as Applied Computing continues developing an AI model designed for complex industrial systems. Rather than focusing on a single type of industrial data, the startup is targeting the broader challenge of connecting the many different data sources found across a facility.
Thousands of sensors create huge volumes of data
A single oil, gas or petrochemical facility can contain thousands of sensors. These sensors can measure a wide range of conditions, including temperature, pressure, velocity and viscosity.
The result is a large and complex data environment. Information is generated across different systems and formats, while engineering documents and scientific data add further layers of complexity.
For operators, the value of this information depends on being able to understand it together. Applied Computing says that the fragmentation of industrial data is a significant hurdle for companies trying to use it to support operating decisions.
The startup is therefore building its AI model to work across the different forms of information that make up an industrial facility.
Callum Adamson says operators struggle to combine existing information
Applied Computing co-founder and CEO Callum Adamson said operators already collect much of the information they need.
The problem, according to Adamson, is that they struggle to combine sensor readings with engineering documentation and information from physics and chemistry. As a result, large amounts of potentially useful data are not being fully used in operating decisions.
This challenge has created a significant opportunity for technology companies working to help energy businesses manage and understand industrial data. Applied Computing is positioning its foundation AI model as a way to address the fragmentation across complex facilities.
AI model aims to cover the entire plant
Applied Computing’s broader goal is to build an AI model for the entire industrial plant rather than focusing only on one isolated source of information.
That approach reflects the complexity of oil, gas, refining and petrochemical operations, where decisions can depend on information collected by sensors as well as technical documentation and scientific principles.
By targeting the full facility, the startup is working to create an AI system capable of bringing together the different forms of information that operators already have access to but may find difficult to use in combination.
The company’s focus on the sector also reflects the scale of the data challenge facing energy operators. With thousands of sensors in a single facility, the amount of information available can be substantial, but fragmentation can limit how much of it is used.
KBR leads funding round as Applied Computing develops its technology
The Series A round was led by KBR, an engineering company, with Databricks Ventures also participating.
The $20 million investment gives Applied Computing additional funding as it develops its foundation AI model for the oil, gas and petrochemical industry.
The startup is focused on solving a specific problem within industrial operations: helping companies bring together data from sensors, engineering documentation, physics and chemistry so that more of the information available to them can support operating decisions.
Applied Computing’s technology is being developed for oil, gas, refining and petrochemical systems, where the complexity of facilities and the fragmentation of data can make it difficult to use information effectively.
Applied Computing is building AI for complex industrial systems
The company’s approach is centered on the idea that industrial AI needs to work with the full range of information generated by a facility.
That includes sensor data measuring physical conditions, technical and engineering documentation, and information connected to physics and chemistry. Bringing these sources together is a central challenge for operators, according to the company.
Applied Computing is now using its new funding to continue building its AI foundation model for the sector. Its focus remains on helping oil, gas, refining and petrochemical operators make better use of data that is already being generated across their facilities.
Applied Computing has raised $20 million in Series A funding to develop a foundation AI model for the oil, gas and petrochemical industry. Led by KBR with participation from Databricks Ventures, the funding supports the London-based startup’s effort to address the fragmented data environment found across complex industrial facilities.
Founded in 2023, the company is focused on connecting sensor readings, engineering documentation, physics and chemistry information to help operators use more of the data already available to them. Follow the company’s progress as it continues building AI technology for entire industrial plants.
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