Designing modern vehicle dispatch systems

A McKinsey survey of 250 leading shippers and logistics providers reveals that almost all companies (87% of shippers and 93% of providers) intend to increase investment in new technologies despite recent industry turbulence. The reason is the proven value of such solutions: respondents report productivity improvements across logistics processes, averaging 10–20% in the short term and 20–40% over the first four years. The only factors tempering enthusiasm are the cost of these products and the complexities of integrating them properly with existing solutions, such as the Transportation Management System (TMS) and trackers.

Understanding vehicle dispatch systems and their core components

What a vehicle dispatch system is

Despite the rapid digitalisation of logistics, many fleet operators still seek clarity on what dispatch software is and how it actually differs from traditional tracking and fleet management systems.

In practice, it serves as the digital foundation, essentially the central nervous system for all fleet and transportation operations. Technically, it’s a platform that tackles three core complex challenges:

• operational monitoring and fleet tracking;
• resource allocation and cost reduction;
• real-time communication and delivery process coordination.

Modern software for dispatching allows for extending far beyond traditional logistics capabilities. Typically appearing as a centralised dashboard, it integrates with external services, advanced dispatch tracking software, and, increasingly, AI models that significantly enhance operational advantages. The most sophisticated systems can collect and process data on thousands of vehicles, drivers, and cargo every minute, providing dispatchers with quick access to accurate information and response times under one second in mission-critical situations.

Essential dispatch system features

Ideally, transportation dispatch systems should provide a comprehensive set of capabilities for such complex industries as logistics and passenger transport, which drive their continuous development and functional expansion. Nevertheless, even in their basic configuration, they typically include at least the following functions:

• automated dispatch scheduling and route optimisation;
• real-time vehicle tracking;
• communication with drivers and customers;
• integration with corporate systems;
• operational metrics monitoring;
• fleet management and maintenance;
• alert and notification systems.

The role of dispatcher apps in fleet management

Mobile dispatch technology has become essential in modern fleet operations, transforming dispatchers from desk-bound operators with microphones into agile managers. Dispatch system apps enable real-time fleet oversight from any location, allowing coordinators to respond to incidents, reassign vehicles, and communicate with drivers instantly – all without being tied to a central office. 

We’ll return to the technical details of mobile-first dispatch apps later in the article.

Automation in vehicle dispatch: from automatic dispatch systems to autonomous dispatching

From manual control to the first computerised systems

The evolution of dispatching dates back to early postal services and trading expeditions: mounted messengers delivered critical communications that determined the outcomes of battles, and merchant caravans established supply chains through ancient trade routes.

From the first civilisations until the 20th century, approaches evolved and scaled but remained largely manual: even after the telegraph and radio were invented, cargo and much correspondence were still transported physically, along with all the inefficiencies that entailed.

The breakthrough came with the first computerised vehicle dispatch systems in the 1970s. Alongside emergency services in the US, commercial transport implemented its own Fleet Management Systems (FMS), enabling the automation of specific delivery processes and inventory tracking, as well as improved coordination. These systems began to gradually replace paper-based logistics and radio communication, opening up unprecedented opportunities for data processing and analysis.

The era of automated dispatch systems

Amid the widespread adoption of personal computers in the 1980s, dispatcher software continued to evolve. Over time, it developed from the basic tracking tools of the early computerisation era into advanced platforms capable of automatically suggesting or even making decisions. Meanwhile, the late 1990s marked a turning point with the commercial rollout of GPS tracking technology. These advancements not only made accurate delivery forecasting a standard but also laid the foundation for today’s AI-driven dispatch platforms with autonomous capabilities.

The transition to AI-driven autonomous dispatching

Wise Systems, a Cambridge-founded fleet AI optimisation company and one of the leading providers of intelligent dispatching analytics, describes five developmental levels of autonomisation based on the SAE classifier adapted for logistics.

In brief, according to the proposed system:

• Level 0 corresponds to fully manual dispatching with paper-based and Excel planning;
• Level 1 – software-assisted annual/semi-annual planning of static routes based on historical data with limited automation and daily manual adjustments;
• Level 2 – partial automation: the system independently plans critical functions, manages fleet constraints and delivery time windows with minimal daily planning requirements;
• Level 3 – conditional full automation with dynamic real-time routing from order receipt to delivery, with dispatchers required only for exception management;
• Level 4 – highly autonomous system for known scenarios, utilising big data and system self-learning, able to resolve most exceptions without requiring dispatcher intervention;
• Level 5 – complete autonomisation through AI with adaptation to any scenario without human involvement.

According to industry data, most organisations still operate at Level 1, while others are beginning to transition to Level 2. A smaller group is experimenting with Level 3, where real-time decisions are delegated to the system, and humans only intervene when anomalies occur. This makes Levels 3 and 4 the logical next stages in dispatch evolution as AI systems continue to advance in contextual awareness and adaptability. 

Unlike earlier solutions focused on isolated tasks, a modern automated dispatch system can anticipate operational needs and optimise responses before incidents occur. By applying machine learning to operational datasets, it detects recurring patterns, optimises resource usage, and refines dispatch logic over time. 

Thus, the industry’s goal is not merely to optimise delivery, dispatching, and fleet management processes but to enable systems to continuously improve themselves in a way that, ideally, over time, reduces the need for human involvement to an absolute minimum.

Next-generation software for dispatching: mobile access, cloud infrastructure, and real-time control

Mobile-first architecture

Many modern dispatch systems are built as progressive web applications (PWAs) with a responsive framework. As a result, they run seamlessly on iOS, Android, and desktop devices, with interfaces that automatically adjust to any screen size. This is a practical choice, given that drivers typically access the system on their smartphones, while dispatchers use corporate workstations. Moreover, PWAs help reduce development costs by eliminating the need to create and maintain separate native apps for each platform. This also makes them a smart choice for online dispatch software.

Cloud as the foundation

Cloud services have become the standard for many dispatch management software solutions: they reduce infrastructure costs, simplify deployment and scaling, and help ensure optimal performance during peak load hours. According to industry data, the fleet management platform market is growing at a double-digit rate. It is expected to reach nearly $100 billion by 2034, driven by the adoption of IoT-connected vehicles and the development of 5G, AI, and big data. Generally, these technologies require extensive cloud infrastructure.

On the other hand, many fleets still prefer hybrid architectures, often placing mission-critical functions on local nodes, like MEC, while moving global optimisation and data storage to the cloud.

Advancing real-time control through AI and 5G enhancement

Comprehensive integrations with open APIs are becoming a defining feature of modern systems: the most advanced dispatching platforms can simultaneously exchange data with corporate ERP/TMS (Traffic Management System), innovative city platforms, and other third-party services (maps, payments, insurance systems). For example, integration with city traffic lights using Vehicle-to-Infrastructure (V2I) standards could enable automated route adjustments to match green wave timing, reducing delays. Simultaneously, the system could transmit data to other vehicles – an increasingly crucial capability for next-generation automotive dispatch solutions in connected environments.

Such technologies are already being tested and will continue to develop as edge computing and 5G – key to seamless streaming architectures – evolve to support future system autonomisation. According to an industry report, the ongoing development of 5G and edge computing is expected to increase data throughput and reduce latency while likely enabling improved security through localised data processing. As more devices gain the ability to analyse and exchange data at the edge, cloud dependency is also likely to decrease, leading to more resilient and bandwidth-efficient architectures across connected fleets and logistics networks.

In practice, this means that in passenger transport, for example, IoT sensors and connected systems at bus stops can independently analyse and directly send signals to a bus’ onboard system about dangers, obstacles, or the need to adjust routes. Such use cases are described in research by MDPI.

Peculiarities of specialised solutions for different vehicle types

Bus dispatch software

According to Global Market Insights, the global bus transportation market is growing at an average annual rate of 8.3%, driven by urbanisation trends and a shift toward eco-friendly city mobility. At the same time, municipal fleets operate in complex urban environments that demand high reliability, precise scheduling, and stringent passenger safety standards, all while navigating the constraints of congestion and changing road conditions.

To address these challenges, transportation dispatch systems for buses must combine classic fleet coordination with transit-specific capabilities:

• route planning and optimisation – automatic schedule generation and rescheduling based on real-time passenger demand and traffic density analysis; integration with city traffic management systems; coordination with passenger information displays and e-ticketing platforms;
• real-time GPS monitoring with passenger-focused analytics – including route adherence control, unauthorised stop detection, passenger capacity monitoring, and integration with contactless payment systems for boarding validation;
• fleet and vehicle condition management – fuel and charge levels for hybrid/electric buses, component wear monitoring with predictive maintenance alerts, accessibility equipment status (ramps, audio announcements), and climate control optimisation;
• driver and shift management – dispatch scheduling and shift planning based on passenger flow patterns, automatic assignment by route complexity and driver qualifications, fatigue monitoring through ADAS, and performance tracking against punctuality metrics;
• communication and incident management – rapid dispatcher-driver communication, automatic emergency alerts, passenger complaint integration, and coordination with city emergency services;
• security and compliance systems – integration with panic buttons, CCTV surveillance with facial recognition capabilities, accessibility compliance monitoring, and integration with city-wide security networks;
• continuous analytics through IoT and telematics – passenger flow analysis, revenue optimisation, environmental impact tracking, and integration with smart city infrastructure for traffic light prioritisation.

Companies such as INIT GmbH (Germany), Trapeze Group, GIRO Inc. (Canada), and Verizon Connect Reveal (USA) are among the leading developers of such solutions for public transport.

Truck dispatch software

Besides the active growth of e-commerce and last-mile delivery, one of the main drivers for the development of autonomous truck dispatching will be the autonomisation of freight transport itself. According to McKinsey forecasts, Europe and the US are facing acute driver shortages, meaning autonomous transport will become more noticeable from 2027 and could become an everyday reality by 2040. This will likely intensify the demand for IoT and GPS-associated functions to support autonomous piloting in dispatch software.

Currently, however, software for dispatching trucks requires deep integration with accounting, legal, and regulatory constraints (such as ELD/IFTA in the US and similar EU regulations), as well as fine-tuning for carrier operations and growing logistics demands.

Thus, beyond functions similar to public transport, truck dispatch software might include:

• ELD and HOS compliance integration – driver hours monitoring with cross-border regulation handling, automated rest period scheduling, compliance reporting for multiple jurisdictions, and preparation for autonomous vehicle operation logs;
• document and cargo-specific management – automated international consignment notes in compliance with the CMR convention and bill of lading generation, dangerous goods documentation, customs clearance integration, temperature and humidity logging for sensitive cargo, and real-time cargo status updates;
• load-specific planning with advanced constraints – considering freight type (FTL/LTL), weight distribution across axles, bridge and tunnel restrictions, hazardous materials routing with emergency response coordination, and multi-modal transport integration;
• regulatory compliance systems – temperature monitoring with deviation alerts, cargo securing verification, driver certification tracking, vehicle inspection scheduling, and cross-border documentation automation;
• financial optimisation and cost control – real-time fuel efficiency analysis, toll optimisation, automated invoicing with cargo-specific billing, insurance integration, and carbon footprint tracking for ESG reporting.

Among the leading developers of dispatch technology solutions for this sector are Axon Software (Canada), Motive, ITS Dispatch, ProTransport, McLeod Software, Verizon Connect, and Trimble Transportation (USA).

Shuttle dispatch software

Although autonomous shuttles may still seem costly or futuristic, McKinsey data show considerable interest and even a willingness to choose them over private cars today. Use cases already include corporate, campus, and airport transfers, as well as short-range urban routes and event-based services. That’s why software for dispatching this type of on-demand transport must support its flexibility, with a strong emphasis on safety, personalisation, and remote supervision. 

As a result, the most critical functions of shuttle dispatch software include:

• intelligent routing and load optimisation – real-time route calculation based on passenger requests, machine learning-based demand forecasting, dynamic capacity allocation, and optimisation for comfort, accessibility, and ride-sharing preferences;
• integration with booking and payment systems – flexible user experience via calendar and event-based booking, group trip management, and contactless payment with expense tracking;
• communication and real-time updates – GPS navigation with passenger estimated time of arrival (ETA) sharing, proactive delay notifications, voice communication with passengers through integrated calling systems, multi-language support, and integration with corporate communication platforms;
• safety and feedback systems – multi-level alert system by criticality (critical/moderate/informational), passenger fall detection through motion sensors, remote operator intervention capabilities, post-ride quality scoring, and incident response automation;
• telemetry and autonomous vehicle management – comprehensive sensor monitoring (LiDAR, radars, cameras, odometry, IMU, GPS), energy consumption optimisation for electric shuttles, predictive maintenance based on usage patterns, remote diagnostic capabilities, and complete remote shuttle control in critical situations.

Currently, approximately 20 autonomous shuttle brands worldwide are designed for delivery services or passenger transportation, including Nuro (USA), Yutong and UISEE (China), Moove (Germany), Ohmio (New Zealand), BOLDLY (Japan), and others. Each of these relies on shuttle dispatch software, whether developed in-house or provided by a third-party vendor.

User experience concerns in vehicle dispatch systems

Designing for dispatcher efficiency and clarity

A typical challenge operators face is the fragmented apps and interfaces within dispatch systems. This often happens when standalone tools from different vendors (e.g., GPS tracking, messaging, or task management) are connected via APIs but lack a unified interface and consistent data flow. As a result, dispatchers end up juggling multiple screens and consoles, and even after training, they can forget how to perform specific actions or monitor key parameters.

Nevertheless, modern vehicle dispatch systems are becoming unified platforms, consolidating key functions into a cohesive workspace with logical navigation, organised dispatcher responsibilities, and visually emphasised priorities. For example, Samsara, Verizon Connect, and Fleet Complete have implemented a similar integrated, modular design. 

One of the critical components of such platforms could be an alert module that classifies all incidents by criticality levels, helping dispatchers automatically focus on genuinely important issues while indicating the required response level. Similar alerts can be batched or grouped to prevent flooding the user with notifications. 

In addition, several separate interface enhancements can significantly improve dispatcher efficiency:

• сontextual tooltips and inline help: hovering over buttons or fields provides immediate, concise explanations, helping dispatchers understand functions quickly and minimise errors;
• smart defaults and predictive inputs: when dispatchers assign tasks, the system suggests drivers based on availability and qualifications, minimising manual entry.

Information overload and visual hierarchy

Even within unified dispatch system software platforms, users often struggle with information density. Critical details can easily get lost among dozens of simultaneously displayed fields and lists.

Progressive disclosure offers a practical solution: dispatchers see essential trip details (status, ETA, priority) first, then access additional telemetry, event history, or video feeds on demand. This approach maintains focus while preserving task context without perceptual overload.

Role-based information access further optimises workflows: managers and dispatchers often require different data sets, and interfaces should reflect these distinct needs. Dashboards can be customised so users only see data relevant to their role or task.

Norwegian UX/Product Designer Ricardo Lamego describes in his Zoom Courier Software case study how he applied some of the principles mentioned above to redesign a dispatch control panel.

According to the author, the company had faced the classic problem of fragmentation: dispatchers had to switch between separate tables for drivers and tasks, resulting in missed deliveries and unbalanced workloads. Although all the data was available, it lacked the necessary context for making quick decisions.

Lamego created a unified, card-based interface to replace the previous tabular layout. Critical information – including status, priority, and time – was displayed immediately, with details revealed upon click. A two-colour coding scheme enables dispatchers to instantly assess driver availability and task status, while drag-and-drop makes assignments easier. Cards automatically minimise after assignment to display only relevant data.

According to the designer, the concept was well-received and prompted a complete restructuring of the company’s product strategy around the new dispatch screen.

Adapting UX for mobile-first interfaces

Designing dispatch system apps and interfaces for mobile devices requires fundamentally different visual organisation and interaction approaches than desktop versions. The primary challenge involves providing dispatchers and drivers with quick access to essential information on limited screens, often while they are in motion or under stress.

Thus, the prioritisation described above should manifest not only structurally but also visually through size and colour. Critical information, such as breakdowns and route deviations, should be emphasised through significant elements and contrasting colours, while secondary data appears more restrained.

A one-handed operation is equally important. Essential elements should be placed in the thumb zone, the lower third of the screen. Swipes work better than buttons for switching between trips, while long presses activate deliberate actions, such as making emergency calls or confirming arrival. Consider left-handed user adaptations, too.

Adaptive typography matters as well: font sizes should scale with screen size and lighting conditions, while applications should adjust brightness or enable night mode for driver safety.

Harvard Shuttle app: applying mobile design principles for high-stress environments

The Harvard Shuttle app redesign project, while primarily focused on campus bus passengers rather than operators, demonstrates interesting approaches to combining UX optimisation with user stress management.

The developers had to design an interface that worked under constraints of time, uncertainty, and movement, similar to those in which dispatch applications themselves operated. Notably, student interviews revealed frustration from inaccurate arrival information and navigation difficulties. Since users “expected a simple process and did not want fancy features that complicated usage,” the team implemented adaptive design features that prioritised simplicity and reduced user anxiety:

• visual prioritisation of critical information: large font for bus numbers and audio announcements for quick identification among multiple similar vehicles at stops;
• minimalist map-based instructions with navigation to stops;
• numbers instead of colours and symbols to protect user privacy and improve compatibility with screen readers;
• smart system nudges: automatic warnings when selecting suboptimal stops with better route suggestions, and walking recommendations during system overload or when significantly faster;
• automatic route adaptation to accessibility parameters – for example, if a wheelchair platform is required;
• vibration signals and notifications 2 minutes before arrival and upon bus arrival for operation in noisy and distracting environments;
• duplicate voice notifications for users with disabilities;
• adaptive cancellation and modification logic: varying trip modification options depending on time until departure, including penalties for cancellations less than 2 minutes before arrival to prevent abuse.

Implementing some similar features in dispatch software would help, for example, in quickly locating a driver’s vehicle within a large logistics complex during an emergency, and, more generally, reduce cognitive load for more efficient response management.

A flexible system of adaptive modifications and abuse prevention would likely also be in high demand in dispatch applications within the logistics sector. Several dispatchers on Reddit described an acute problem where their dispatch software failed to handle unpredictable human behaviour in unexpected situations adequately. This created vulnerabilities to system deception and a constant risk of operational breakdowns.

Building scalable dispatch systems: technical trade-offs

According to Maxim Leykin, CTO at Bamboo Apps and our technical advisor on this topic, the key factors to consider when designing dispatch management software include:

• low latency;
• data consistency;
• high availability;
• dispatching optimality;
• scalability;
• integration.

Based on this, Maxim outlines several typical development challenges of such systems along with potential solutions.

Challenge 1: choosing between strong consistency and high availability

In dispatch software, this means balancing the choice between keeping all nodes updated with the same data at the same time and ensuring the system continues to operate even if some nodes fail.

However, according to the CAP theorem, any distributed data store can guarantee at most two of the three desirable properties: Consistency, Availability, and Partition Tolerance.

In this situation, possible architectural solutions may include:

• event-driven architecture, where all system state changes generate events published in message brokers and consumed by other services for data updates;
• data replication technologies, either provided by cloud DBMS (such as Google Cloud Spanner or Amazon DynamoDB) or manually configured partitioning and multi-master replication;
• CQRS (Command Query Responsibility Segregation) – a pattern that separates read and write operations for data storage into separate data models or databases.

In practice, similar approaches are implemented in Uber’s dynamic pricing architecture.

In their research, the company explains how they address the CAP dilemma by prioritising availability and partition tolerance over strict synchronous consistency. The system is built on an event‑driven platform: each state change is transmitted as an event via Kafka and processed by Flink streaming services. Data is replicated across regions in an active‑active configuration and gradually converges to a consistent state. Write and read streams are separated: events are ingested and processed in a single pipeline. At the same time, analytics is handled by dedicated Pinot and Presto services based on principles close to CQRS.

Challenge 2: finding the trade-off between speed and optimality

Another typical problem in dispatch systems is that finding the absolute optimal solution, such as the shortest route or most efficient resource utilisation, is often computationally intensive and time-consuming. This complexity influences algorithm choice (exact optimisation, rule-based algorithms, machine learning for predictive dispatching, heuristics), data structures, and pre-computation levels. 

It’s also essential to define what constitutes “good enough” solutions for specific systems and when they outperform the strictly optimal ones.

Lyft’s ETA system demonstrates the latter point. Instead of optimising exclusively for the most accurate arrival time estimates, the company introduced a new metric: reliability, defined as the percentage of trips that arrive within the expected time window. The company developed a machine learning model that evaluates multiple ETA options simultaneously, predicting the reliability of each ETA option and selecting the one that meets predefined service level agreements (SLAs). This approach prioritises reliable “good enough” predictions that minimise passenger cancellations while maintaining real-time responsiveness at scale.

Challenge 3: achieving low latency

Often comes at the cost of overall system throughput (the number of dispatches processed per second) and vice versa. Prioritising one necessitates:

• different queuing mechanisms (Kafka is often preferred for high-throughput, low-latency streaming of large volumes of data, while RabbitMQ can achieve low latency for queuing but might have higher overhead for extremely high throughputs compared to Kafka);
• processing paradigms (e.g., event-driven vs batch);
• communication protocols (REST, gRPC, WebSockets, MQTT);
• resource allocation.

Renowned technology evangelist Kai Waehner, an expert in Apache Kafka and data streaming, explains how Penske Logistics navigates this trade-off in fleet operations. 

Processing 190 million daily IoT messages from over 400,000 vehicles, Penske replaced batch processing with an event‑driven architecture on Kafka and Flink. This enabled real‑time GPS, diagnostics, and sensor data processing for proactive maintenance and delivery coordination. 

Kafka’s distributed design ensures high throughput with low latency, while Confluent Cloud reduces overhead, allowing the team to focus on business logic. Consequently, the system has prevented over 90,000 potential roadside incidents, demonstrating how event-driven technologies enhance responsiveness and efficiency at scale.

Challenge 4: choosing between monolithic and microservice architectures

This decision is driven mainly by the same requirements as in other systems with similar needs: low latency, high availability, and scalability. Typically, developers choose a monolithic approach for relatively small systems with well-defined requirements and microservices for large, scalable systems with multiple integrations and high availability requirements. 

We would especially mention the Strangler fig pattern approach, where developers start with a monolithic system and subsequently migrate it to microservices by first separating the most critical and high-load components. This is good for developing flexible integration APIs with external systems such as telematics, ERP, EV charging, and others.

American food delivery company DoorDash describes its own experience of architectural evolution. As demand increased, the company transitioned from a Django-based monolithic Python application to a microservices architecture. Previously, interdependent services created compound availability risks, with order fulfilment and dispatch logistics forming “Golden Workflows” where failures could cascade across the entire system.

To overcome these issues, DoorDash implemented zero-downtime migration techniques and reliability patterns, such as request fallback and fail-open behaviours, which helped decouple critical dependencies and isolate failures. As a result, the system gained independent scalability and significantly improved service reliability.

Case study: how we built Dispatcher, a mobile-first dispatch system for autonomous shuttles’ control

At Bamboo Apps, we set out to rethink conventional dispatch systems for autonomous transport, which typically rely on multiple monitors to handle streams of sensor data. Our objective was to demonstrate that all essential functionality could be delivered via a single portable device without compromising access to critical information.

That’s how Dispatcher came to be: a cross-platform Flutter application designed for 10.2-inch tablets and compatible with both Android and iOS operating systems. The system integrates with Google Maps, functions offline using GPS, and processes data in real-time from radars, LiDARs, and six cameras (three external and three internal).

One of the main technical challenges was synchronising video streams across different components of the application. This was addressed by developing independent video controllers that operate separately from the app screens, eliminating delays when switching views.

As a result, the application enables dispatchers to plan routes with automated arrival time and battery usage forecasts, monitor shuttle status and location, and intervene in emergencies. It processes critical alerts, from passenger falls and emergency calls to low battery warnings and route deviations, instantly. At the same time, built-in voice communication through onboard cameras enables direct contact with passengers, eliminating the need for additional hardware.

Conclusion

Modern software for dispatching is undergoing a fundamental shift, not only from Excel-based tools but even compared to the digital systems used just a few years ago. Technologies that once seemed like science fiction are now being actively implemented, delivering tangible benefits for fleet operations. 

This evolution is closely tied to the rise of vehicle connectivity, particularly in the realm of autonomous transportation. The need to monitor and process ever-growing sets of metrics and performance indicators in the changing logistics and transportation environment drives dispatch solutions to become more advanced and feature-rich. These, in turn, lead to more complex system architectures and place greater demands on the supporting infrastructure. 

Thus, designing such systems is a demanding task: beyond the technical complexities themselves, there is a persistent risk of overloading the dispatcher with a cluttered, unintuitive interface. That’s why addressing these challenges takes not only technical expertise but also a deep understanding of user needs and operational realities – and that’s precisely what we aim to deliver.

At Bamboo Apps, we help companies design and deliver intelligent fleet management solutions and dispatcher tools – from concept to deployment. Our expertise spans intuitive mobile UX, IoT integration, and scalable architectures with AI support.

Whether you’re building a mobile dispatch system, expanding a cloud-based vehicle dispatching solution, or modernising your scheduling and dispatching software, we’re here to help you move toward smarter, more autonomous operations.

Request a consultation to discover how we can help your project achieve dispatch excellence.