Real-world information infrastructure for AI systems

AI systems increasingly depend on sensors, operators, infrastructure networks, commercial systems and first-party data. PlaceRouter is building the infrastructure to discover, evaluate and route real-world information by reliability, freshness, coverage and context.

request
Idle
Architecture illustration
GOES-19 full-disk satellite view of the western hemisphere with cloud systems over the Americas and Atlantic
One physical world

AI can reason.

It still needs reliable information about what is actually happening in the physical world.

Image · NOAA GOES-19
One layer, many domains

Thousands of information systems.

AI systems need facts about the physical world. Those facts live in systems with different freshness, reliability, coverage, authority, cost, latency and rights. PlaceRouter is horizontal infrastructure across them.

Domain status ranges from active research to exploring. See every domain

Where PlaceRouter sits

AI systems on one side. The systems that measure and operate the world on the other.

Models provide the intelligence. Sensors, operators, government systems, data providers, commercial APIs and first-party systems hold the information. PlaceRouter is the layer that discovers, evaluates and routes between them.

Source evaluation

Finding an API is not enough. A source has to be evaluated.

A source is not simply good or bad. It performs differently by geography, time, field, hardware class, horizon and freshness. Different requests need different sources.

Aerial view of a highway interchange surrounded by industrial buildings, the kind of area several traffic, weather and sensor sources describe differently
requirement

Freshness under 1 minute

Architecture example · illustrative values
Reliability · Freshness · Coverage · Authority · Cost · Latency · Rights · Provenance
How PlaceRouter is designed to work

Ask for information. Not an API.

One requirement across thousands of locations. Different fields of the answer come from different sources, each chosen for that field, that place and that moment.

4,000 locations · field

Traffic speed

Target architecture · synthetic locations
Traffic speedCommercial traffic feed
Road closureState DOT, where it has coverage
VisibilityWeather network
Surface conditionRoad sensors, weather network elsewhere
ProvenanceAttached to every field
Research

We test routing strategies against observed outcomes.

Routing real-world information requires measurement. PlaceRouter runs experiments across domains and reports what worked, what did not, and what remains inconclusive.

15,318paired transit observations · controlled freshness replay
Active research

As transit information was replayed with increasing delay, error rose measurably. Finding a source that contains a field is not enough; the age of the information matters.

freshdelayed →error ↑ error worsened as information aged
Weather580observations · 20 stations

Local adaptation showed signal; naive per-station transfer can hurt.

Air quality10,459observations · 45 stations

Naive routing did not beat the raw best source; hardware-class calibration transferred.

Source probe32sources · 17 verticals

Access, authentication and stability differ substantially by domain.

Hospitality44hotels · 110 pages

Structured availability varies dramatically by field.

Work with PlaceRouter before the universal API exists.

An Intelligence Pilot maps, connects, benchmarks and routes the sources behind one information requirement, and returns data with provenance.

About intelligence pilots
A National Data Buoy Center weather buoy on the water beside a shipImage · NOAA NDBC
Data network

Measure or operate part of the world?

PlaceRouter is also building relationships with sensor networks, operators and data providers whose information AI systems depend on.

The data network

AI can reason.
PlaceRouter connects it to the world.

Building AI that depends on real-world information, or producing information AI depends on? Talk to us.