Planespect

A drone surveys every surface for paint damage, dents, and other defects. Planespect logs each finding across the fleet, predicts deterioration, and directs inspectors to the right spot at the right time.

How It Works

The Problem

Aircraft surface inspection is stuck in the manual era

Certified inspectors still climb scaffolds and cherry pickers to examine fuselage panels, wings, and nacelles. Each inspection starts from scratch: no spatial record, no fleet-wide learning.

Patterns that span hundreds of aircraft—zone-specific wear, climate-linked paint breakdown—stay hidden because data never leaves individual maintenance files.

Manual inspection burns hangar hours, varies by inspector, and obscures fleet-level trends.

No data sharing between aircraft or over time means no learning from patterns

Three Layers of Intelligence

From raw imagery to maintenance decisions

Each layer adds value the previous one cannot deliver by itself.

Capture

Autonomous Visual Inspection

An autonomous drone records complete surface imagery inside the hangar. Routes generated from OEM CAD geometry guarantee identical coverage on every visit.

Built-in quality gates flag blur or gaps, triggering automatic recapture. Controlled lighting tames glare on painted aluminum.

Airframe-level surface quality visualization

Understand

Surface Intelligence Software

Each scan extends a persistent surface record. Software aligns structural zones, tracks change, and predicts how every defect will evolve—before the next check.

A 3D digital twin fuses imagery, OEM data, and inspection history into a living model for every tail. Fleet-wide baselines then estimate when any defect will hit its allowable limit.

Decide

Inspection-to-Decision Engine

The system outputs priority-ranked worklists driven by structural context and predicted growth. For example, a fast-growing paint flaw near a fatigue-critical splice tops the list, while a stable scuff on a fairing can wait.

OEM references and fleet-wide models make that triage computable. Each flagged zone arrives with a recommended action and a forecast intervention date.

Defect review overlay with structural context

Why Planespect Is Different

We do more than take pictures: Beyond surface-level data

Most drone inspection tools only show that something looks wrong. Planespect links each defect to real structure, maintenance history, and engineering data—so inspectors know what matters, what can wait, and what is getting worse.

	Typical Drone Inspection	Planespect
What it finds	Visible marks or damage in photos	Visible defects tied to the actual aircraft structure
What the location means	A spot on an image	An exact aircraft zone with known structural importance
How priority is set	Visual severity: How bad it looks	How bad it looks and how important that area is
What history shows	Separate inspection snapshots	Time-based records showing whether damage is stable, spreading, or accelerating
What happens next	Operators decide when to re-inspect	The system forecasts when a finding is likely to reach allowable limits
How results are explained	A confidence score from software	Measured findings with structural context for inspector review
What operators learn over time	Issues on one aircraft at a time	Fleet-wide patterns, including repeat problem areas and degradation trends

Read the full comparison →