By: Elena Mart

The beaches of Brazil’s coastline once fell silent after sunset, avoided by locals and tourists alike. Today, those same stretches of sand pulse with life well into the evening – families strolling, vendors selling, communities thriving. The transformation didn’t require massive policy changes or cultural shifts. Instead, it came through something seemingly mundane: streetlights – tens of thousands of them, intelligently networked and managed.

For Leeland Zhang, the CEO of Spesland, witnessing this transformation firsthand became a defining moment. “To see a lively beach at night when before it was dead silent, to know of the decrease in crime rate and how much the city could save taxpayer money with improved technology – that’s when I understood I was making a global impact,” Zhang recalls.

As the founder of Spesland, Zhang has dedicated his career to transforming urban infrastructure management through artificial intelligence. Unlike many tech entrepreneurs focused on consumer apps or enterprise software, Zhang addresses a challenge that often goes unnoticed but plays a crucial role in modern city life.

The Invisible Crisis in City Management

Cities worldwide are drowning in data they can’t use. From streetlights and traffic signals to water systems and public transportation, digital infrastructure generates staggering volumes of information. A single mid-sized city might have 100,000 streetlights, each collecting dozens of data points every few minutes.

“If you do the math,” Zhang explains, “that’s 100,000 lights times 65 data points, updated every five minutes. You end up with hundreds of millions of data points annually, just for street lighting.”

This data represents both tremendous opportunity and overwhelming challenge. Hidden within these numbers are patterns that could prevent infrastructure failures, optimize resource usage, improve public safety, and save millions in taxpayer dollars. But traditional systems make accessing these insights nearly impossible for the government employees who need them most.

From Code to Conversation

Spesland’s solution lies in bridging this critical gap through natural language processing. Rather than requiring specialized technical knowledge to query complex databases, their technology allows government employees to ask questions in plain language simply.

“You can ask in natural language some precise data that you want – maybe streetlight 455, and then about some operational parameter at a certain point in time,” Zhang explains. The AI then translates these questions into the necessary technical queries, retrieves the relevant information, and presents it in an accessible format.

This transformation from coding to conversation represents far more than convenience – it democratizes access to critical information. Maintenance crews can immediately identify failing infrastructure before it affects citizens. Emergency response teams can coordinate resources during weather events. City planners can optimize resource allocation to save taxpayer money.

What makes Spesland’s approach uniquely powerful is its ability to retrieve and interpret data. “After an issue happens, most of the time, the government knows that it happened, but they don’t know why or what caused it,” Zhang notes. “It takes a lot of effort on their side to contact engineers. It’s a very long process.”

Spesland’s AI changes this dynamic entirely. The large language model is trained to know what sort of data to look at for a specific issue and then what conclusion to make from that data. That cuts out the entire process for governments so they can solve the issue as fast as possible.

Security Without Compromise

Perhaps most remarkably, Spesland achieves this breakthrough while maintaining unprecedented security standards. Unlike most AI companies pushing cloud-based solutions, Spesland’s system can be deployed entirely on-premise, ensuring sensitive city infrastructure data never leaves government control.

“One of the hardest technical points of our product is that it’s deployed entirely locally,” Zhang explains. “None of the data needs to pass through our hands. The data cannot leave maybe one or two rooms, so it can’t travel elsewhere.”

This requirement demanded significant innovation. AI models gain power through size and access to vast cloud computing resources. Zhang’s team had to develop compact yet powerful models that could run on limited local hardware while maintaining accuracy.

Global Impact Through Local Understanding

This approach has enabled Spesland to expand rapidly across six continents. Their technology now supports Latin America, Europe, the Middle East, and Asia-Pacific cities. Unlike many tech startups focused on rapid scaling through standardization, Spesland adapts its solution to local needs while maintaining a universal approach to human-data interaction.

“In every single country, they have different compliances, different things you must navigate,” Zhang notes. This global-local balance has allowed Spesland to create an impact across widely varying urban environments.

The applications extend far beyond lighting management. Smart buildings, bridges, water systems, and other critical infrastructure all benefit from the same approach – making complex systems manageable through natural conversation.

A Future Where Cities Speak Our Language

Zhang’s vision extends far beyond current applications. “Our vision is to fundamentally transform how humans interact with data across every industry and organization globally,” he says. “Long-term, we aim to create an ecosystem where natural language becomes the universal interface for all enterprise systems – imagine every employee, from field workers to executives, having an AI assistant that understands their domain and can execute complex operations through simple conversations.”

This transformation aims to reshape the relationship between citizens, government workers, and the increasingly complex systems that power modern urban life. By making technology accessible, Spesland is helping create cities that are not just “smart” in their ability to collect data, but truly intelligent in how they use it.

“Our drive comes from two fundamental aspirations,” Zhang explains. “First, knowing that our work improves citizens’ lives through safer, more reliable public services and enhanced social welfare. Second, we’re passionate about revolutionizing human-data interaction – making technology accessible and intuitive for everyone.”

As cities worldwide face mounting challenges from climate change to population growth, this ability to speak the language of their data may prove essential to creating more resilient, efficient, and livable urban environments for all.

Disclaimer: This article is for informational purposes only and does not constitute professional, financial, or technical advice. The views expressed are those of the author and interviewee and do not necessarily reflect the official stance of Spesland or affiliated entities. Readers should conduct independent research or consult relevant experts before making decisions based on the information provided.

Published by Stephanie M.