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1/21/2026
Compiled by Julie Sneider, Senior Editor
Artificial intelligence (AI) is no longer a concept reserved for tech labs and futuristic prototypes. Across the rail industry, AI is being integrated into practical tools that improve safety, efficiency and decision-making in the field.
Suppliers are increasingly turning to AI-driven solutions to address long-standing challenges, and one emerging example is RailSentry, an intelligent grade crossing monitoring system developed by Herzog. RailSentry represents a shift in how railroads approach grade crossing safety, company officials said in an email.
Traditionally, crossings have relied on fixed warning systems, passive signage or reactive measures following incidents. RailSentry adds a proactive layer by using AI to create a digital twin of the track environment that is continuously monitoring crossing activity in real time, Herzog officials said.
The system is designed to detect unsafe behaviors, such as vehicles stopping on tracks or bypassing gates, and capture actionable data that can be used by railroads and municipalities to reduce risk before incidents occur.
The impact of this technology is already measurable, according to Herzog. In 2025, RailSentry installations were deployed across multiple railroads, including Caltrain and Trinity Railway Express, bringing the total number of units to 13.
One of the most notable installations took place at a crossing in Burlingame, California, which is widely regarded as one of the most hazardous crossings in the country, Herzog officials said. Since RailSentry was installed, the crossing has experienced zero incidents, highlighting how AI-driven monitoring can translate directly into improved safety outcomes, they said.
Beyond individual crossings, RailSentry reflects a broader trend among rail suppliers toward embedding intelligence into infrastructure. Rather than replacing human expertise, AI is being used to enhance it, Herzog officials said. Data collected by systems such as RailSentry helps engineers, safety teams and planners better understand risk patterns, evaluate crossing designs and prioritize improvements.
As AI continues to gain traction in railroad applications, its value lies less in novelty and more in results. Systems like RailSentry demonstrate how intelligent technology can be deployed today to solve persistent safety challenges, reduce incidents and provide railroads with clearer insight into their operations, Herzog officials believe.
For suppliers working in the trackwork space, the message is clear: AI is no longer an add-on. It is becoming a core component of how safer, smarter rail infrastructure is built and maintained, Herzog officials said.
As railroads continue to expand the volume and variety of track inspection and condition data that they collect, the challenge has shifted from access to usability. Engineering and maintenance teams often work across multiple datasets, each with its own structure and terminology.
VisioStack is addressing this challenge by pairing its integrated search engine capability with an AI-powered Search Builder that simplifies how users interact with complex track data, company officials explained.
Global Search serves as VisioStack’s unified way to query, combine and visualize railway assets and condition data. Users can filter, join and summarize information across inspection results, images and operational datasets, then save searches and share links so teams can collaborate around the same findings. This creates a consistent foundation for analyzing track conditions and supporting planning decisions, VisioStack officials said.
Building on that foundation, the company’s AI Search Builder enables users to describe what they need using plain language. Requests such as identifying defects near bridges over a recent time period, locating rail segments likely to require replacement in a future year or highlighting gaps in track geometry coverage can be queried directly.
The large language model evaluates the data available in Global Search and translates user intent into an appropriate query by selecting relevant data types, fields and filters.
This approach reduces two common obstacles in track data analysis: navigating data complexity and constructing technically correct queries, VisioStack officials said. By helping users build and refine searches more quickly, the AI Search Builder supports faster investigation and better prioritization during planning and maintenance cycles, they said.
Rather than replacing engineering judgment, VisioStack’s use of AI is focused on making existing data easier to access and apply to real world trackwork decisions.