Route Optimization API
Generate dispatch-ready multi-stop routes across multiple depots in seconds. Supports 50+ hard and soft constraints including time windows, vehicle capacity, shift hours, and regulatory compliance.
Managing routes across multiple depots, territories, and fleets is a coordination problem most APIs weren't built to solve. NextBillion.ai's Route Optimization API handles 50+ hard and soft constraints, multi-depot planning, and real-time re-optimization — delivering dispatch-ready routes in seconds with predictable per-vehicle pricing that won't punish you for scaling.

A complete API suite for multi-location route planning, dispatch, live tracking, and territory management at any scale.
Generate dispatch-ready multi-stop routes across multiple depots in seconds. Supports 50+ hard and soft constraints including time windows, vehicle capacity, shift hours, and regulatory compliance.
Calculate real-time and historical travel times and distances between up to 5,000×5,000 origin-destination pairs in a single API call — the foundational cost matrix for every routing decision.
Automate the handoff from planning to execution by pushing optimized routes directly to driver apps and fleet management systems in real time, eliminating manual re-entry and dispatch delay.
Instantly match drivers to tasks using flexible dispatch logic, shift timings, location familiarity, and order priority — with real-time dynamic reassignment when conditions change on the road.
Pre-segment thousands of delivery stops or field service tasks into proximity-based, capacity-aware clusters by depot catchment or territory — reducing routing complexity before optimization runs.
Monitor every vehicle and field agent in real time with up to 1-meter accuracy. Feed live location data back into route optimization for dynamic ETA recalculation and on-road exception alerts.
Most routing APIs cap matrix sizes at 25×25 and charge per API call — a model that collapses under the weight of multi-location operations with hundreds of depots, thousands of daily stops, and constant re-optimization cycles. NextBillion.ai's Route Optimization API was purpose-built for this reality: supporting unlimited depots, 50+ routing constraints, and per-vehicle pricing that keeps costs predictable no matter how frequently your planning engine runs. Whether you're coordinating last-mile delivery across regional hubs, managing field service territories coast to coast, or building a logistics SaaS platform for enterprise customers, the API handles the complexity you actually have.

See how global logistics operators and transportation platforms cut costs and scale operations with NextBillion.ai.
Here's what sets NextBillion.ai apart for multi-location businesses that can't afford routing bottlenecks.
Per-vehicle or per-order pricing means seasonal spikes and re-optimization cycles never trigger runaway API bills.
Time windows, multi-depot origins, truck dimensions, hazmat rules, and driver shifts — all handled in a single optimization call.
Dedicated solutions engineers provide hands-on support from trial onboarding through production — not just documentation links.
Go live within a week with cloud-agnostic deployment on AWS, GCP, or Azure, plus on-premise options for data residency needs.
The founders and engineers behind the world's most flexible routing API platform.

Co-Founder
Ajay Bulusu is a co-founder of NextBillion.ai, bringing deep expertise in geospatial technology and large-scale location platform engineering. His journey began in 2017 as part of Grab's pioneering Geo team, where he helped build the location infrastructure underpinning one of Southeast Asia's largest mobility and logistics platforms. That experience revealed how critical — and how underserved — accurate, flexible location technology was for real-world operations. Driven by a mission to democratize sophisticated location intelligence, Ajay co-launched NextBillion.ai in 2020. Under his technical leadership, the platform has scaled to serve 150+ businesses globally, optimize over 10.9 million deliveries and field tasks, and deliver more than $11 million in documented cost savings to customers.

Co-Founder
Gaurav Bubna drives product innovation at NextBillion.ai, bringing a proven track record of building successful products from scratch across ride-hailing, food delivery, and logistics. Before co-founding NextBillion.ai, Gaurav held product leadership roles at Grab, Ola, and Morgan Stanley — organizations where location technology and operational efficiency are existential. His time on Grab's Geo team alongside Ajay and Shaolin crystallized the vision: logistics and field service businesses needed enterprise-grade location intelligence without prohibitive per-call pricing. Since launching NextBillion.ai in 2020, Gaurav has led the product evolution that has helped the platform navigate over 557 million miles and raise $34.25M in venture capital from investors including Lightspeed and Microsoft's M12.

Co-Founder
Shaolin Zheng is a co-founder of NextBillion.ai, contributing deep technical expertise in geospatial systems and scalable mapping infrastructure. Like his co-founders, Shaolin's formative experience building location technology for Grab's Geo team gave him firsthand insight into the operational gaps that generic mapping solutions leave unaddressed — particularly for field service and logistics businesses operating at scale. His engineering perspective shapes NextBillion.ai's core philosophy: location technology must be customizable, cost-predictable, and production-reliable. The platform he helped build now powers GPS tracking, route optimization, and field service intelligence for over 150 businesses worldwide, with on-premise deployment options and SOC 2 Type II and ISO/IEC 27001:2013 certifications ensuring enterprise-grade security.
The multi-depot vehicle routing problem (MDVRP) involves assigning and sequencing delivery stops across a fleet of vehicles that start and end at different warehouse or depot locations. The goal is to minimize total travel distance, time, or cost while respecting constraints like vehicle capacity, time windows, driver shift hours, and regulatory rules. It's significantly more complex than single-depot routing because the optimizer must simultaneously decide which depot serves which stops and how to sequence each vehicle's route.
Talk to a NextBillion.ai solutions engineer for a live demo and free technical consultation.
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Tell us about your fleet size, depot structure, and routing challenges. A NextBillion.ai solutions engineer will respond within one business day with a tailored demo and integration roadmap.
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