Air Traffic Control — Candidate Assessment

Know who belongs in terminal controlarea controloceanic controlthe tower before training starts.

Falcon is a web-based ATC simulation and assessment platform that gives Air Navigation Service Providers objective, data-rich reports on candidate aptitude — at a fraction of the cost of traditional screening.

>99%
Separation violation detection rate
5+
Scored simulation runs per candidate
20+
Distinct performance metrics tracked
Web
No installs — runs in any modern browser

ATC training is expensive. Attrition is unpredictable.

Traditional ATC screening relies on aptitude tests and interview panels that struggle to predict real-world performance. Training programs cost tens of thousands of dollars per candidate — and washout rates can exceed 50%.

ANSPs need a better signal before committing to full training pipelines.

Study-level simulation, accessible in a browser.

Falcon delivers a realistic, browser-based radar simulation environment backed by our Separation Integrity Engine and AI-powered voice recognition layer. Every session generates a detailed, objective candidate report — no evaluator subjectivity required.

01
Radar Simulation

A study-level terminal area radar environment built in Unity and deployed to the browser via WebGL. Candidates work real traffic scenarios with aircraft following realistic flight dynamics.

WebGL · Unity · Real-time
02
Separation Integrity Engine

Our proprietary SIE detects >99% of separation violations in real time. It predicts future conflicts using predicted intercept vectors (PIV) and enforces all horizontal, vertical, and terminal-area separation rules.

Proprietary · Patent-pending
03
Voice Recognition & Inference

Candidates issue real voice commands. Our LLM-based inference layer understands non-standard phraseology, corrects transcription noise, and scores communication quality against ICAO standards.

LLM · ICAO · Real speech
04
Objective Scoring

Each run produces a weighted numerical score incorporating error severity, planning efficiency, communication quality, and multitasking performance — with full audit trails per aircraft and per transmission.

Automated · Auditable
05
Candidate Dashboard

Evaluators get a structured report covering simulator scores, learning metrics, skills grading across 15+ categories, and AI-generated interview questions tailored to each candidate's specific performance gaps.

AI-generated · Per-candidate
06
Browser-Native

The entire platform runs in a modern browser — no downloads, no IT overhead, no specialist hardware. Candidates can be assessed remotely or on-site with nothing more than a headset and a laptop.

Zero-install · Cross-platform

Every candidate, quantified.

Here's what an evaluator sees after a candidate completes their simulation sessions.

Falcon — Candidate Assessment Dashboard
Candidate
#852
Simulator Score
68.4
Learning Score
82.4
Overall
75.4
Simulator Runs 5 sessions
Run Sep Loss Critical Significant Minor Score
1 0 0 0 0
100
2 2 1 2 1
47
3 2 1 3 2
21
4 0 1 2 3
74
5 0 0 1 1
93
Skills Grades
Track milesA
Transmission efficiencyA
Number of vectorsB
Proper phraseologyB
Readback monitoringA
Rate of speechA
Repeated commandsA
Proper prioritizationF
Strip updatesA
Most Common Errors
Radar separation 3/1000×4
Aircraft cleared to wrong waypoint×3
Incorrect turn direction×3
Late frequency change×2

Three steps from candidate to decision.

01

Candidates complete simulation sessions

Using only a browser and headset, candidates work through a series of progressive radar scenarios — managing real flight strips, issuing voice commands, and handling live traffic.

02

Platform scores every action automatically

The SIE and voice inference layer score each session in real time — tracking separation violations, communication quality, planning efficiency, and multitasking across all aircraft simultaneously.

03

Evaluators receive a structured report

A detailed per-candidate dashboard shows scores, skills grading, common error patterns, and AI-generated interview questions — giving hiring panels everything they need to make a confident decision.

Built by engineers obsessed with aviation safety.

Matthew Zhang
Co-founder

Leads platform architecture and the Separation Integrity Engine. Deep background in real-time systems, simulation fidelity, and ATC operations.

Ian Tan
Co-founder

Leads the voice recognition and LLM inference layer. Background in natural language processing, speech systems, and machine learning at scale.

David Hood
Co-founder

Leads product, infrastructure, and ANSP partnerships. Background in software engineering and deploying safety-critical systems for regulated industries.

University of Waterloo Waterloo Institute for Sustainable Aeronautics AC:Aerospace Accelerator

Falcon is incorporated in Canada and headquartered at 200 University Ave W, Waterloo, ON N2L 3G1.

Ready to see Falcon in action?

We work directly with ANSPs to tailor the assessment pipeline to local airspace rules, traffic profiles, and phraseology standards.

Request a demo — desk@falconatc.com