Hybrid AI + Quantum Optimization
for India’s Logistics

We turn combinatorial chaos into on-time deliveries: VRP with time windows, fleet & shift planning, and returns consolidation — solved in minutes, not hours.

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On-time in-full

−10–20% lateness

−8–12% km/stop

+3–5% drops/vehicle

SLA compliance

CO₂ reduction

# QUBO: minimize xᵀQx
# Variables: x_{i,t,v} ∈ {0,1}
# Visit once  : (Σ_{t,v} x_{i,t,v} − 1)²
# Slot unique : (Σ_i x_{i,t,v})²
# Capacity    : (Σ_i d_i Σ_t x_{i,t,v} − cap)²
# Objective   : distance + time-window penalties

Built on graph theory pioneered by Euler — now supercharged with quantum-inspired solvers.

Run a 4-week pilot

Scope: 2 depots · 20–50 vehicles · 1–2k stops/day · VRP-TW, returns, and shift optimization. Success = −8–12% km/stop, −10–20% lateness, +3–5% drops/vehicle.

The problem we tackle

Exploding combinations

Tens of thousands of daily stops, vehicles, depots, and time windows make exact search infeasible.

Dynamic reality

Traffic, rain, no-shows, and priority orders invalidate plans every 30–60 minutes.

Multiple objectives

Minimize km, meet SLAs, balance fleets, and respect capacity — all at once.

Our solution