Oil plants use 8% of data: Applied Computing raised $20M to fix it
industrial ai startup applied computing raised $20m led by kbr to launch orbital, a model combining time-series, physics, and llms to predict facility states and compress weeks-long investigations into minutes.
applied computing secured $20m in series a funding led by engineering firm kbr to expand its industrial ai model, orbital, which targets the oil and gas sector's massive data fragmentation problem. the model uniquely fuses time-series data, physics-based constraints, and large language models to predict facility states, allowing technicians to run simulations that previously took days or weeks. the company reports reaching double-digit millions in arr within 18 months and counts wipro and kbr as partners, integrating with kbr's insite 3.0 platform for ammonia production. this move pressures entrenched industrial software incumbents like aspentech and aveva by offering a specialized, physics-aware ai solution rather than generic automation.