Your supply chain doesn't wait.
Neither does Helixor.

When a constraint changes — a driver goes over hours, a lane closes, a supplier drops capacity — traditional solvers restart the entire optimization from scratch. Helixor's incremental execution engine detects exactly which parts of the solution are now infeasible and repairs only those, leaving the rest intact. The repaired solution arrives in milliseconds, not minutes.// incremental DAG re-execution: only recomputes what changed

Traditional solvers enforce hard constraints during the solve — but many heuristics and ML-based planners check feasibility after the fact and patch violations. Helixor's two-phase constraint architecture pre-compiles hard constraints as binary masks applied to the solution space before optimization begins. Infeasible solutions never emerge. Every route, every allocation, every schedule is guaranteed feasible before it is issued.// hard constraint satisfaction built into the optimizer, not bolted on after

Static solvers return a solution and stop. Helixor's reasoning layer continuously accumulates what works — which strategies succeeded on which problem signatures, which constraint patterns recur — and incorporates that operational knowledge into future solves. The optimization gets faster and more accurate on the problems it has seen, without any retraining cycle or model deployment.// continuous improvement from verified operational outcomes, no retraining required

VRP, inventory optimization, production scheduling, BOM planning, demand forecasting, trade compliance — in traditional stacks these live in separate systems that can't jointly optimize. Helixor's universal constraint architecture handles all of them as a single interconnected problem, allowing a route change to propagate through inventory, a demand signal to update scheduling, and a tariff change to re-evaluate sourcing — simultaneously, in the same solve.// cross-domain optimization across the full supply chain, not siloed point solutions