Stop scaling operations with headcount. Start with AI that works inside your TMS.
We help 3PLs, freight brokerages, and carriers ship AI into the work that matters, exception handling, dispatch, and the back office, integrated with the systems you already run and built for your ops team to actually use.
Where Pre-AI logistics operators stall.
Most operators who haven’t yet deployed AI hit the same wall in the same place. The pattern is almost identical, operation after operation.
You know exactly where the problem is.
Your ops team is over capacity. Exception handling burns hours every shift, your best dispatchers carry too much in their heads, and the back office grows with the business.
You’ve heard every AI pitch.
Every TMS vendor and AI consultancy has shown you a deck. The promises are big. The actual deliverables, and the cost to reach them, stay vague.
Your team is skeptical.
Your dispatchers and ops managers have seen technology projects come and go. They want to see something working before they buy in, and until they do, no deployment takes hold.
Your CFO wants the ROI math first.
Headcount cost avoided, hours saved, exception resolution time: those numbers need to be quantified and defensible before anyone signs a check.
The decision stalls.
The pain stays. Headcount keeps absorbing the load. Another quarter goes by while competitors put AI into their dispatch and exception handling.
We exist to break that cascade. →
How we work.
A structured 60–90 day engagement built for ops-driven decisions: operations audit first, then TMS integration, then a shipped feature, with measurement and team enablement baked into every stage.
Operations Audit & Mapping
We spend time on the floor with your ops team and dispatchers, find the highest-friction workflows, and score every AI candidate on operational ROI, integration effort, and how often the work actually repeats. You get a ranked list, not a wishlist.
Every candidate workflow, scored before a line of model code.
We map and prioritize opportunities against operational ROI, integration effort, and how often the work repeats, specific to your operation. You walk away knowing exactly what to automate first, and why.
The panel that proves what the deployment is actually delivering.
Measurement systems and runbooks quantify the value to your operation and to your CFO, hours saved, exceptions auto-resolved, cost avoided, so the case for the next deployment makes itself. Evidenced, not asserted.
What you walk away with.
Concrete artifacts, a TMS-integrated deployment, and a trained ops team, not slides.
A first production deployment, shipped, not piloted.
What a Pre-AI engagement looks like when it lands: an exception-handling agent live across facilities, instrumented from day one.
A multi-region operator was triaging tens of thousands of inventory and shipment exceptions every week across stores, warehouses, and dropship partners. Manual reconciliation kept missing the SLA window, and the team was burning out.
We deployed an exception-handling agent with a full audit trail, a real-time reconciliation pipeline, and an operations dashboard. The system handled routine exceptions end-to-end and escalated only the genuinely unusual cases to humans.
Engineering under real-world load, before it was an AI problem.
Cryenx’s engineering legacy is in performance-critical, real-time systems for brands where reliability under load wasn’t optional: Disney, Coca-Cola, Apple, LEGO, Warner Bros, SXSW. Different industries, same posture: systems that work the first time, scale under real load, and integrate with what’s already there.
That experience, engineering under real-world constraints, integration with existing systems, observability discipline, is exactly what logistics AI requires. For your operation, that means AI your ops team can actually use, layered onto your TMS, with measurable ROI from day one.
