Coverage of Parabole and causal AI in industry, and the channels where product and company updates are published as they happen.
Moving causal-model generation from CPUs to the NVIDIA GH200 Grace Hopper Superchip cut it from roughly ten hours to under a minute, making real-time production planning and multi-objective optimization practical at industrial scale.
Steve Banker on Georgia-Pacific’s deployment of Parabole’s causal AI, using best-practice knowledge and plant data to build a causal model of the operation, rather than a pattern-matcher on past events.
An ARC Advisory Group conversation with Parabole’s co-founder and COO on moving optimization from a single unit to the whole enterprise, and why a validated causal model is what makes that scale.
Vivek Murugesan’s taxonomy of the Industrial AI market: DataOps, platforms, advanced analytics, application suites, asset and process optimization, machine vision and more. Parabole is placed under “Agentic Operations”: the companies building decision intelligence from the ground up with knowledge graphs, causal reasoning and autonomous agents.
LNS Research’s buyer’s guide to the Industrial AI Platforms and Advanced Analytics categories. Parabole appears in the “Agentic Operations and Knowledge-Driven Industrial AI Startups” honorary mentions, the next-generation providers LNS expects to fold into future editions of the matrix.
Presented with Georgia-Pacific: how one causal model turns siloed production, maintenance and asset-performance planning into coordinated agents. Written up on the Causal inference page.
Session recaps from Parabole at the Connected Worker conference, capturing the operator and engineer knowledge that a causal model needs. Posts are on the Parabole LinkedIn page.
The live delivery timeline and feature roadmap for TRAIN, with maturity status and target quarters. Access is provided to customers and evaluation partners.
Written and recorded material on causal inference for heavy industry, published to Insights as it is released.
Bring the engineer who knows the process. We will walk through the causal model behind it.