How AI-Ready Street Segment Data Powers Better Location-Based Decision-Making

One thing I’ve learned after decades in the location data world, it’s that accurate street information has a unique way of reducing friction.

I see it most clearly in business decisions. A franchise evaluating a new location needs to know more than an address – it needs to know what competitors are nearby, how traffic flows, and whether customers can realistically stay long enough to make a visit worthwhile. If parking is limited or requires a longer walk in hot or cold climates, that matters. If pickups and deliveries are routinely delayed by congestion on a specific street segment, that matters too.

The same principle shows up in everyday life. I’ve taken family trips through Europe where having reliable road data meant fewer wrong turns and far fewer “spirited discussions” in the car about which exit we should have taken.

What I’m getting at is this: good street network data creates clarity—and each segment matters. And clarity, in any context, takes the noise out of decision-making.

That need for clarity, particularly in the AI era, is exactly where our new data enrichment offering, StreetPro™ Discover comes in – delivering AI-ready street-level intelligence.

Organizations today are racing to operationalize AI – deploying LLMs, conversational interfaces, and intelligent agents across workflows. But even the most advanced AI systems are only as good as the data behind them.

And when it comes to street segment data? Most enterprises are working with datasets that were never meant for natural language querying or automated reasoning. Attributes arrive as cryptic abbreviations, numerical codes, or deeply interlinked fields that require spatial expertise to unravel. It’s powerful data but is largely inaccessible, virtually locked behind formatting that only human specialists can interpret.

The result is a bottleneck: AI systems can’t make sense of the data, and leaders can’t easily act on it in AI-driven decision-making scenarios.

StreetPro™ Discover was designed to break that bottleneck.

Our goal was simple: turn street level complexity into clarity – at speed and at scale – by making street segment data AI-ready without sacrificing depth or accuracy. Not by simplifying the data itself, but by transforming how it’s expressed, delivered, and integrated into LLM-powered workflows and AI agents operating in real-world environments.

Why Street Data Still Feels Harder Than It Should

Talk to any data analyst, data scientist, or business leader working with street and location data, and they’ll tell you the same story. To understand what’s happening on a single street segment – traffic density, road type, restrictions, address ranges – they often work with complex “raw” data formats that requires complex joining of tables to access street segment data and street-level attributes to:

• Decode opaque field names and numeric values
• Stitch together multiple disconnected attributes
• Run computationally heavy spatial queries across an entire region
• Spend hours translating data for teams who need clear answers, not columns of codes

This isn’t because street data should be hard. It’s because it was originally engineered for navigation engineers or GIS professionals – not conversational AI, not business stakeholders, and certainly not LLM-powered workflows.

When you’re building AI-ready data pipelines, every one of those steps adds friction. And it prevents organizations from connecting street level intelligence to address level decision-making – despite the fact that many of their highest-value use cases depend on exactly that nuance.

We built StreetPro™ Discover on a simple belief: street data should accelerate decisions, not get in the way.

So instead of requiring people (or AI systems) to interpret the data, StreetPro™ Discover interprets it first as AI-ready geospatial data that both humans and machines can understand.

Turning Street Segments Data into Something AI (and Humans) Can Actually Use

At its core, StreetPro™ Discover performs a deceptively simple transformation: it expresses street segment attributes in human-readable, semantically rich descriptions – while preserving the structure, accuracy, and depth of the underlying data.

But it’s not just formatting, it’s a fundamental redesign of how street data interacts with the modern data ecosystem. It reflects a need I hear constantly – whether from data teams or business leaders who just want a straight answer without pulling in a specialist.

StreetPro™ Discover replaces inscrutable codes with text that both humans and LLMs can understand. Want to know:

• Which streets have high traffic exposure?
• What might complicate deliveries to a specific property?
• How road type, density, or peak speeds vary across a neighborhood?

Ask in natural language and get an immediate answer. This works because the data itself is built for semantic search and RAG workflows. It’s data that speaks the same language as the AI systems (and remember, systems include people) using it.

As a result:

• Site selection becomes clearer and more accessible.
• Delivery and last mile planning stop being reactive.
• Urban planning and infrastructure investments get sharper.
• Risk and underwriting decisions get more grounded.
• Follow-on questions become more nuanced and site specific.

When street data becomes transparent, decision-making becomes faster, more confident, and more consistent.

Linking Directly to Address-Level Context

Earlier in my career I worked at TomTom, and that’s where I first experienced the impact of highly accurate street data firsthand.

That’s part of what makes this release so exciting. Through Data Link for TomTom, users can easily connect StreetPro™ Discover to address-level insights through our unique, persistent identifier, the PreciselyID. This links street segment intelligence to a broader ecosystem of enrichment attributes, building a frictionless bridge between:

• Traffic density and property details
• Road characteristics and demographics
• Street restrictions and place information
• Modeled attributes and risk indicators

It means that a single prompt — “What might cause delivery delays for this address?” — can now surface an explanation that spans both the street data and the broader data ecosystem.

This linkage matters because most location-driven decisions don’t happen on the street. They happen at the address.

How We Finally Cut the Heavy Lift Out of Street Data

One of the biggest surprises for people new to street data is how much heavy lifting usually sits between having it and actually using it. Traditionally, you needed big spatial engines, long processing windows, and the patience of a saint.

I’ve spent enough years in this space to know that nothing slows momentum like waiting for a region‑wide spatial job to finish running – especially when the question you’re trying to answer is about one address on one street.

StreetPro™ Discover cuts out that drag.

By aligning street data to the H3 hex grid, you can target exactly the locations that matter – not the hundreds of thousands that don’t. Think of it as zooming directly to the square mile that matters instead of scanning a whole atlas.

That shift alone means faster processing, better accuracy, and more cost-efficient analysis. This dramatically accelerates time to value for teams, reducing the effort required for feature engineering, enrichment, and spatial analysis that used to demand significant expertise and manual stitching.

Closing the Gap Between Street Data and Real Decisions

If there’s a theme that cuts across how AI is evolving, it’s this: actionable insights win.

Organizations don’t need more data. They need Agentic-Ready Data that accelerates decisions instead of slowing them down. Data that moves at the speed of their workflows. Data that AI can reason with just as easily as people can.

StreetPro™ Discover was built to deliver that advantage.

It removes friction – the cryptic fields, the manual joins, the spatial workloads – and replaces it with human-readable, AI ready intelligence. It brings together the richness of street-level data and the pinpoint accuracy of address-level context. And it does all of this in a way that scales across the real-world applications where location insight matters most.

When I think back to those European drives where accurate street data kept the peace in the car, I’m reminded that good data doesn’t just reduce arguments, it improves outcomes. StreetPro™ Discover is designed to bring that same clarity to the enterprise: turning every location decision into a faster, smarter, more confident one.

If AI is the engine, StreetPro™ Discover is the street-level intelligence that helps it navigate. Visit the StreetPro™ Discover data guide to learn more.