Qavis builds AI + quantum-hybrid optimization for operations too complex for off-the-shelf tools. Industries

Complexity looks different in every vertical.
The math underneath doesn't.

Pick your industry to see the use cases that matter most. Each links to the full solution one-pager.

Which one are you?

Four different titles, one problem.

Check below — we may have already heard your problem.

Production Planner

I rebuild the schedule every time something moves.

Setups, changeovers, machine availability, due dates — you hold it all in one spreadsheet and your head. One breakdown and the whole sequence unravels. You re-plan by hand, you know it isn't the best sequence, and you ship it anyway because there's no time to do better.

Logistics / Fleet Planner

By the time the routes are right, the day has changed.

Time windows, multi-depot, cargo compatibility, driver hours. You build the routes the night before and spend the morning patching them by phone. Every patch is a guess. Every guess costs fuel, hours, or a missed window.

Operations Director

I find out about the problem after it's already cost me.

You see the results at the end of the month, not the decisions that caused them. Service levels slip and no one can tell you exactly where. The plan looked fine — but the plan was never the thing that ran.

Head of Supply Chain / COO

Our planning capability is one person deep.

The operation runs because someone knows it by heart. That knowledge isn't in a system, and it walks out the door at 6pm. Every year the operation gets more complex and the tooling doesn't.

Air cargo is a network optimization problem in disguise — bookings, flight loads, ULD builds, and live-window decisions that all affect each other. Qavis optimizes the full cargo planning chain, from pre-departure allocation to the tactical window before uplift.

Use cases we solve

Allocation

Tail Assignment

Aircraft-to-rotation assignment under maintenance and turnaround rules.

One-pager (PDF) Scheduling

Crew Scheduling

Pairings and rosters under duty-time regulations and disruptions.

One-pager (PDF) Scheduling

ULD Scheduling

Position unit load devices where cargo actually needs them.

One-pager (PDF) Scenario

Disruption Response Simulation

Pre-computed recovery plans for weather and technical delays.

One-pager (PDF)

The cargo optimization chain

3

Flight Loading Optimization

Pre-departure planning
  • Rebooking
  • Call-forward
  • Rerouting
  • Flight allocation
ObjectiveAllocate bookings to flights across the network, optimizing feasibility.
OutputFlight-level logical load plans
4

ULD Build Optimization

Physical planning layer
  • Mixed vs destination ULDs
  • Intact vs break strategy
  • Transfer complexity vs utilization
  • ULD configuration
ObjectiveConvert flight loads into optimal ULD builds balancing utilization & handling.
OutputULD build instructions
5

Tactical / Live Window Optimization

D-1 to departure
  • Alert management
  • Flight checks
  • Offloads / rebooking
  • Call-forward
  • Gateway optimization
  • Backlog management
ObjectiveReact to real-world changes & protect network performance.
OutputFinal uplift decisions

The problem underneath: fragmented data

  • Multiple data sources & formats
  • Data gaps, delays & inconsistencies
  • Limited end-to-end visibility
  • Manual effort & reconciliation
  • Suboptimal local decisions
  • Lower utilization & lower revenue
  • More disruption & operational risk
⚠Fragmented data = local optima, not network optimal. Qavis unifies revenue, operations, and warehouse data into one optimization layer.
Book a working session →

Your vertical isn't listed?

If the operation has hard constraints and a combinatorial core, the engine transfers. Rail, postal, and transit are already in our range.

Book a working session →