석유 플랜트는 데이터의 8%만 활용: Applied Computing, 이를 해결하고자 2,000만 달러 유치
산업 AI 스타트업 Applied Computing이 KBR 주도의 투자로 2,000만 달러를 유치하며 Orbital을 출시했습니다. 이 모델은 시계열, 물리학, LLM을 결합해 설비 상태를 예측하고, 몇 주 걸리는 조사를 몇 분으로 단축합니다.
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.