Parabole's TRAIN is a self-service causal inference platform for industrial operations, modeling causal decision agents for refineries, pipelines and plants. It captures how an operation actually behaves, so its highest-stakes decisions hold up under conditions no one has seen before.
Parabole began in 2014 with language and semantic technologies. In 2018, we applied that foundation to causal reasoning for industrial operations.
Years of deep-tech development, real-world deployment, and learning alongside industrial enterprises led to TRAIN, our self-service causal inference platform for industrial operations.
Today, leading enterprises use Parabole to understand how their operations actually behave, reason through conditions they have never seen before, and make high-stakes decisions with greater confidence.
The first decade built the science. Now we are scaling the business.
“If you know what causes what, you have solved your problem.”
Causal inference on a live process is not one discipline. It sits where research, plant engineering, operations, and platform work meet.
The people who build the structural models and the method behind the baseline, currently being prepared for peer review.
People who know how a unit is actually run: hydraulics, thermodynamics, recipe, the constraints that move with the operating point.
People who have carried the pager, sat through an alarm flood, and made the call on a developing condition at 3 a.m.
People who deploy and operate the platform auditably within each customer's environment, identity architecture, and upgrade process.
Three co-founders, together since 2014, with strategy and sales leadership added as the platform moved into production, and, behind them, a research and engineering organization at the junction of process engineering and deep computer science.
A category creator in industrial causal AI, Rajib has led Parabole for 12 years, building the company and its TRAIN platform from the ground up. He previously led enterprise transformation, architecture, and risk programs.
An AI pioneer and accomplished technology architect, Sandip leads Parabole’s technology vision, intellectual property, and product engineering. He previously led advanced research and embedded-systems initiatives at Samsung and Sasken and holds multiple patents in computing and intelligent systems.
An industrial analytics and enterprise-growth leader, Manesh drives Parabole’s operations, customer delivery, and commercial expansion. He previously led global partnerships and complex strategic agreements at American Express, following product and business-development roles at ICICI Bank and HDFC Bank.
A three-time-exit revenue executive, Jonathan brings more than 20 years of experience scaling enterprise AI, data, and analytics companies. He leads Parabole’s global sales and strategic accounts, following senior commercial roles at Harbr, Mind Foundry, Stardog, Limelight Networks, and Omniture.
Behind the five names is the group that actually builds the platform: a research and engineering team working where process engineering meets deep computer science: causal discovery and identification, probabilistic and structural modeling, refutation testing, large-scale optimization, and the systems work to run all of it against live plant data.
It is a deliberately small field. A focused global research and engineering team builds and deploys the platform, and the people who join contribute directly to commercializing causal AI on real industrial problems rather than to a research backlog. The platform is that team’s research made into a product.
Operating executives who ran the decisions this platform is built for.
Former board member, Samuel Son, US Chamber of Commerce, and National Association of Manufacturers.
President, 3M Separation and Purification Sciences Division.
Founder and Research Fellow: industrial AI, causal systems, and enterprise transformation at Georgia-Pacific.
Former executive at The Home Depot, Williams-Sonoma, Levi Strauss & Co., Martha Stewart Living, and Georgia-Pacific: retail, eCommerce, and digital-transformation leader.
We are a focused team working on a long-term problem. Explore our open roles if you want to apply causal inference to real industrial process data.
Causal modeling and the generated baseline. Princeton, NJ or remote.
Deployment and auditability inside customer environments. Princeton, NJ or remote.
Full list on the careers page, or write to info@parabole.ai.
Bring the engineer who knows the process. We will walk through the causal model behind it.