How Roam Network Turns Human Movement Into Navigation Data

As AI systems move out of data centers and into streets, vehicles, and machines, a new problem is emerging. These systems can navigate physical space, but they lack awareness of the digital conditions they depend on. Roam Network was created to close that gap by turning everyday human movement into live, user-owned intelligence machines can rely on.

We spoke with Roam’s co-founder and CEO, Topi Siniketo, about why this missing layer matters, how the network works, and what changes when people own the value their movement creates.

Q1. What made you realise that as AI and autonomy move into the real world, machines are still missing something basic?

Answer: When you look at where autonomy is heading, the gap becomes obvious.

As systems leave controlled environments and start operating in the real world, connectivity and network reliability stop being background concerns. They become part of the environment the machine is operating in.

Machines have become very good at sensing the physical world. But they have almost no awareness of the digital conditions they depend on to function. As systems operate more independently or in groups, that blind spot turns into a real constraint. Scaling autonomy safely means giving machines live digital context, not forcing them to guess.

Q2. You often say Roam isn’t a map, but a navigation layer. What does that mean in simple terms?

Answer: A map shows you where things are. A navigation layer helps you decide what to do next.

Roam doesn’t present a static view of coverage or performance. It shows how reliable the digital environment is right now, where it’s stable, where it degrades, and how those conditions change over time.

For machines, that difference is critical. It allows them to choose routes, timing, and actions based on where they can actually operate safely, not just where they’re allowed to go.

Q3. Connectivity data has existed for years. Why doesn’t it work well for autonomous systems?

Answer: Most connectivity data was never designed for real-time decisions.

It’s based on operator reports, periodic tests, or one-off measurements. That gives you averages, but autonomy doesn’t fail on averages. It fails in specific places, at specific moments.

Machines need to know what’s likely to happen in the next few minutes along the path they’re about to take. That level of precision isn’t possible with static or delayed data.

Trust is the other issue. When data comes from many sources with different incentives, you need a way to verify it. That’s why Roam is built around contributor-measured ground truth, with validation and tamper-resistance built into the system.

Q4. Roam relies on everyday human movement rather than centralized data collection. Why was that approach necessary from the start?

Answer: Because the world changes faster than centralized measurement can keep up with.

Drive tests and snapshots give you fragments of reality. They don’t show how networks behave continuously across neighborhoods, routines, and edge cases.

Everyday movement does. People naturally create dense, ongoing coverage in the places that matter most. That turns the dataset into something living, not something that needs to be refreshed manually.

It also makes the system scalable. Instead of spending capital to measure the world, the network grows through participation, and the data improves as more people contribute.

Q5. Ownership is a strong theme in Roam’s design. Why did you believe people should own the value created by their movement?

Answer: Because that value already exists. People just don’t benefit from it today.

Everyday movement generates signals about how the digital world actually behaves. Those signals are useful to networks and machines, but individuals usually have no visibility or control over how they’re used.

We believed early on that if a network depends on distributed contributions, ownership shouldn’t sit with a single intermediary. It should flow back to the people who make the network useful. That’s what we mean by owning your footprints.

None of this works without privacy. Roam isn’t about tracking people. It’s about measuring conditions. Ownership, privacy, and verification have to be designed together, or the model breaks.

Q6. Roam is already used by telcos. What do they see differently once they have live digital terrain data?

Answer: They see their networks the way users actually experience them.

Instead of relying on delayed reports or aggregated metrics, operators can see where performance drops, how it changes throughout the day, and which locations need attention first.

Because the data comes from real-world movement, this visibility doesn’t require heavy testing infrastructure. That lowers cost and makes continuous monitoring practical.

Connectivity stops being something operators review after problems occur. It becomes something they can observe and manage in real time.

Q7. When you talk to robotics or drone teams, what failures are they trying to avoid with better connectivity awareness?

Answer: They’re trying to avoid getting stuck with no good options.

When connectivity drops today, systems often default to stopping or returning home. That keeps things safe, but it also interrupts operations and limits scale.

Some teams add redundancy by using multiple providers, but that increases cost and complexity and doesn’t scale well to fleets.

What these teams really want is predictability. If a system knows where connectivity will degrade, it can plan around it. That becomes essential as machines start coordinating, sharing tasks, or offloading computation.

Q8. As Roam expands beyond smartphones into vehicles, drones, and dedicated devices, what new layers of intelligence become possible that didn’t exist before?

Answer: You start to see how digital conditions behave across different kinds of movement.

Phones give you broad coverage. Vehicles add insight at higher speeds and along fixed routes. Drones introduce altitude and vertical variation.

The value isn’t more data for its own sake. It’s context. How conditions change with speed, height, and environment is exactly what autonomous and edge systems need to operate reliably.

Q9. Each new contributor strengthens the network. Why does this compounding effect create a data advantage that centralized mapping systems struggle to match?

Answer: Because several things improve at once: coverage, freshness, and trust.

Centralized systems scale by spending more. More tests, more hardware, more updates. They’re often tied to a single operator or platform, which limits neutrality.

Roam is infrastructure, not a provider. As contributors join, the network naturally densifies where activity exists and refreshes itself continuously.

Each new participant improves validation and reduces blind spots. That kind of compounding intelligence is very difficult to replicate from the top down.

Q10. If Roam succeeds at a global scale, how do you expect machine behavior and the role of everyday human movement in powering it to change over the next decade?

Answer: Machines will act with more confidence because they’ll have better information about the environments they operate in.

Instead of assuming connectivity and reacting when it fails, systems will plan with a clearer understanding of where communication is reliable and how conditions are likely to change. That leads to fewer interruptions and more autonomy at scale.

Everyday human movement continues to power that awareness, but in a transparent and structured way. Over time, that also creates a clearer path for returning value and ownership to the people whose movement keeps the system accurate.