About the role
Why we're building TRAIL
We started KorrAI to monitor ground deformation on critical infrastructure using satellite data. Millimeter-scale precision on where the earth moves. But after working with operators and engineers, we hit the real constraint: synthesis.
A closure engineer inherits decades of scattered data. Satellite imagery, water monitoring, geology reports, soil surveys, historical records, regulatory filings. They spend weeks manually reconstructing site history before they can assess remediation options. Regulators re-ask the same questions because there's no traceable record of what was checked and why.
The bottleneck isn't data availability. It's operationalizing all of it: building a workspace where closure teams can ingest multi-modal sources, surface what matters, and produce reports that cite everything. We call it TRAIL.
The thesis
Billion-dollar infrastructure decisions rest on scattered evidence. Mining closure, construction risk, data center siting are high-stakes, high-evidence environments. Being wrong costs regulatory delays, remediation overruns, or worse.
AI won't replace closure engineers or regulators. But AI can do what humans shouldn't: hunt through thousands of satellite images, cross-reference water monitoring across decades, surface anomalies, connect disparate data sources. The closure engineer uses that synthesis to make the actual decision: the one that moves.
The winners in AI-for-infrastructure aren't companies that replace expertise. They're companies that operationalize evidence synthesis so experts can spend their time on judgment, not paperwork.
What we're doing
We're running closure and remediation assessments across tier-1 mine operations in Canada. We're working with regulatory and federal-care closure teams on legacy sites. Our customers are in active discussions for multi-site rollouts across their operating portfolios.
