Miovision Launches Mateo, a GenAI Agent for Traffic Engineering

Miovision

has introduced

Mateo

, a generative AI agent designed specifically for traffic engineering, marking a shift in how cities analyze and manage transportation networks. Built as a native extension of its Miovision One platform, Mateo transforms complex mobility data into actionable insights through a conversational interface, allowing engineers to query systems in plain language rather than manually assembling reports.

The company positions Mateo as the first purpose-built GenAI agent for intelligent mobility operations, targeting a long-standing bottleneck in the industry: the time required to interpret growing volumes of traffic data.

Turning Weeks of Analysis Into Minutes

Traffic departments have become increasingly data-rich, but extracting meaningful insights has remained slow and fragmented. According to industry research cited by Miovision, the majority of traffic professionals struggle with the time required to analyze modern performance metrics.

Mateo addresses this by automating data collection, cross-referencing, and analysis across multiple systems. Tasks that once required weeks of manual effort can now be completed in minutes through natural language queries, significantly reducing the operational burden on engineering teams.

Rather than replacing engineers, the system shifts their role. By removing repetitive data work, it allows teams to focus on solving congestion issues, improving safety, and optimizing infrastructure.

A Purpose-Built AI for Traffic Systems

What differentiates Mateo from general-purpose AI tools is its domain-specific design. The system combines large language models with a reasoning engine and agentic tools that can perform multi-step analysis on city-specific datasets.

It is trained on traffic engineering principles and integrates directly with telemetry, camera feeds, and safety metrics, enabling it to:

• 
  Correlate siloed datasets such as signal timing, hardware health, and traffic flow
  


• 
  Generate charts, maps, and performance reports instantly
  


• 
  Provide root-cause analysis for congestion or safety issues
  


• 
  Deliver audit trails that trace conclusions back to original data sources
  

This combination of reasoning and transparency is critical in municipal environments, where decisions must be defensible and aligned with established engineering standards.

From Reactive Operations to Proactive Mobility

Historically,

traffic management has been reactive

. Engineers respond to complaints, analyze incidents after they occur, and make incremental adjustments. Mateo introduces a more proactive model.

By continuously analyzing network-wide data, the system can identify inefficiencies, predict emerging issues, and surface actionable recommendations before problems escalate. It effectively acts as a digital collaborator, augmenting teams with real-time intelligence.

Early testing with municipal partners such as the City of Coquitlam demonstrated the practical impact, with teams reporting significant reductions in analysis time and faster responses to network issues.

Built on an Integrated Mobility Stack

A key advantage of Mateo is its deep integration with Miovision’s broader ecosystem. The company’s platform already combines hardware sensors, video analytics, and cloud-based traffic management tools.

Mateo sits on top of this infrastructure, acting as a unified interface that connects all data sources into a single conversational layer. Instead of navigating multiple dashboards, engineers can query the entire system at once and receive synthesized insights instantly.

This integration also allows the system to bridge gaps between different stakeholders, from engineers and operators to city officials who require simplified, executive-level summaries.

The Future of AI in Traffic Engineering

The introduction of Mateo signals a broader shift toward agentic

AI in infrastructure systems

. Traffic networks are becoming increasingly complex, with growing volumes of sensor data, connected vehicles, and multimodal transportation demands.

AI agents like Mateo point toward a future where cities operate with continuous, real-time intelligence rather than periodic analysis. As these systems evolve, they could move beyond diagnostics into automated optimization, dynamically adjusting traffic signals, prioritizing emergency vehicles, and coordinating entire transportation ecosystems.

More importantly, this type of technology reframes how cities justify infrastructure investments. By translating raw data into measurable outcomes, such as reduced congestion or improved safety, AI-driven platforms can make the impact of transportation systems more visible and quantifiable.

If widely adopted, generative AI agents in traffic engineering could become foundational to smart city infrastructure, enabling urban environments that are not only more efficient, but also more adaptive to the needs of the people moving through them.