Your AI is working. Now make it pay for itself across the whole operation.
We help logistics operators with AI already in production tame the operational reality, cost growth, ops-team friction, integration debt, and the missed optimization wins, then scale what’s working to the rest of the business.
What second-stage trouble actually looks like.
Once AI is live somewhere in your operation, the next set of problems is consistent, and usually clear once you know what to look for.
Five lights. One pattern. We exist to turn them green. →
How we work.
An audit-then-implement cycle built for operational impact, and it doesn’t end at delivery. As you roll AI out to more terminals and more workflows, the loop runs again.
Operations AI audit
We inventory where AI is running across the operation, what it costs, what it delivers, where adoption has stalled, and where reliability incidents come from, so the work starts from evidence, not guesswork.
What you walk away with.
Concrete audit reports, measured optimizations, and the infrastructure to keep what we build running across the operation.
Exception handling at scale: the operational pattern that ships.
A measured outcome from the same operational discipline we bring to scaled AI in logistics.
A multi-region operator needed to triage tens of thousands of weekly inventory and shipment exceptions across stores, warehouses, and dropship partners. Manual reconciliation was missing the SLA window, and the team was burning out.
An exception-handling agent with a full audit trail, a real-time reconciliation pipeline, and an operations dashboard: routine exceptions handled end-to-end, the genuinely unusual cases escalated to humans.
Operational reliability was our discipline long before AI made it urgent.
Cryenx’s engineering legacy is in performance-critical, real-time systems for brands where reliability under real load wasn’t optional: Disney, Coca-Cola, Apple, LEGO, Warner Bros, SXSW. Systems that work the first time, scale under unpredictable demand, and integrate with the infrastructure already in place.
That discipline, observability, integration rigor, real SLAs, and operating under unpredictable load, is exactly what scaled AI in logistics demands once it’s live in your operation. Most AI teams learn it the hard way, in production. We brought it with us.
