Applied Computing raises $20M to build an AI model of the whole plant

Applied Computing has raised $20M around an ambitious industrial-AI thesis: operators need a model of the entire plant, not another isolated dashboard monitoring one machine.

What happened

The London-based company closed a $20M Series A led by engineering group KBR, with participation from Databricks Ventures.

Its Orbital platform combines sensor time-series data, physics-based models and language models to represent the operating state of oil, gas, refining and petrochemical facilities. The system is designed to identify anomalies, investigate likely causes and simulate how a change in one part of a plant could affect other processes.

Applied Computing reports reaching annual recurring revenue in the tens of millions within 18 months, although most customers have not been publicly identified. KBR’s involvement is strategically important because the engineering company has deep industry relationships and experience integrating technology into large industrial sites.

Why it matters

Industrial facilities generate enormous volumes of data, but much of it remains fragmented across sensors, maintenance systems, engineering files and legacy software. That makes it difficult to understand how a local problem affects the wider operation.

A facility-wide model could help operators reduce downtime, improve energy efficiency and make safer operational decisions. It could also give experienced engineers a faster way to investigate complex problems.

The bigger picture

Industrial AI is attractive because even small improvements can create significant economic value. It is also difficult: plants differ, data quality can be poor and incorrect recommendations can carry serious safety consequences. Applied Computing must prove that Orbital can be deployed reliably across varied facilities and that its simulations are trusted by engineers responsible for real-world operations.