Computer vision identifies objects.
MetricsAI gives them physical meaning.

MetricsAI connects recognized objects—starting with vehicles and roadside infrastructure—to verified real-world measurements such as length, width, height, weight, and geometry. Existing vision systems use this data layer to improve depth estimation, scale awareness, clearance analysis, spatial reasoning, and safety decisions.

Scope

The metrics data layer. Nothing else.

MetricsAI is infrastructure that plugs into vision systems you already have.

What we don't build

  • × Cameras or camera hardware
  • × Autonomous vehicles
  • × General object detection models

What we provide

  • A structured library of verified physical object measurements
  • An API that links recognized objects to real-world metrics
  • The data layer that makes existing computer vision systems physically aware
Problem & Solution

Detection tells you what. It doesn't tell you how big, how far, or how safe.

The Problem

Vision systems lack physical grounding.

A detection model can label a car, a pole, or a curb—but it doesn't know the object's true dimensions, weight, or geometry. Without that grounding, depth estimates drift, scale is ambiguous, and clearance or collision decisions rely on guesswork.

The Solution

Verified metrics, resolved by identity.

MetricsAI maps each recognized object to verified real-world measurements—length, width, height, weight, and geometry. Known dimensions anchor depth estimation, calibrate scale, and give spatial reasoning a factual base for safety decisions.

How It Works

Three steps to physical awareness.

01

Identify the object

Your existing vision system detects and classifies an object—a vehicle make and model, a sign, a curb, a pole.

02

Retrieve verified metrics

The MetricsAI API resolves that identity to structured, verified measurements: dimensions, weight, and geometry.

03

Improve spatial understanding

Known measurements sharpen depth estimation, scale awareness, clearance analysis, and downstream safety decisions.

metrics.resolve() verified
{
  "object_type": "vehicle",
  "make": "Toyota",
  "model": "Camry",
  "year": 2024,
  "length_mm": 4920,
  "width_mm": 1840,
  "height_mm": 1445,
  "curb_weight_kg": 1585,
  "source_confidence": 0.98
}
Use Cases

Built for systems that operate in the physical world.

Fleet Cameras

Robotics

Autonomous Systems

Mapping

Smart Infrastructure

Long-Term Vision

A scalable physical intelligence layer for the real world.

Starting with vehicles and roadside infrastructure, MetricsAI is building toward a continuously expanding platform of verified physical object data—serving every system that needs to understand the world, not just see it.

Object Detection Object Identity Physical Metrics Spatial Intelligence
Contact

Make your vision system physically aware.

Get in touch

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