📊 29K iOS Apps Tracked Updated 4 min ago ⚡ Revenue Forecasts
Create Account 🔑 Sign In
Home ⚡ Optimize
23
BU Score
Niche

Morse Decoder AI

2.0 ✍️ Editor
✍️ BU Analytics Review

Morse Decoder AI is an iOS app from Yuriy Kvasha in the Utilities category, currently rated 2.0★ across 4 ratings. Initial signal reads as mixed reviews — supporters praise core features while critics cite stability/value gaps.

Our BU Score puts it at 23Niche (micro-niche or pre-traction stage). For a Utilities app, that means micro-niche or pre-traction stage.

Track changes month-over-month in the Performance section below — live snapshot history and revenue forecast included.

📊 Performance Tracking LIVE

Loading…
idle
0%
Rating
Reviews
Forecast Revenue / mo
Snapshots tracked
0
since first record
Range:

💰 Forecast Revenue / mo

MODEL
Revenue forecast computed from BU's 234 trigger model on each snapshot. Calibrated against ground-truth from 58 verified-revenue apps.
🔬Forecast Breakdown — Why This Estimate?Top 5 of 5 triggers
Our ML model uses 200+ signals from public data. These are the most influential for this app:
Paid app ($2.99)METRIC
+$2,800
Below-average rating (2.0★)METRIC
−$1,500
Single-language (English only)METRIC
−$400
Pre-traction phase (4 ratings)METRIC
−$300
Growing app (2y old)METRIC
+$200
METRIC = structural app data · REVIEW = mined from user reviews · ✓ VERIFIED = Stripe-verified anchor (TrustMRR)

📈 Reviews Growth

LIVE
Cumulative review count from first BU snapshot. Each point = a tracked update.

⭐ Rating Trend

LIVE
Average rating evolution. Updates with each new review batch.

🗓️ Snapshot Timeline

HISTORY
Each bar shows a tracked update and the metric delta from the previous snapshot.
App Specs
🔐 Own this app? Claim & verify MRR →
💾 60 MB🔞 4+📱 iOS 12.0+🔖 v1.1🔄 updated 2y ago🌐 EN📂 Utilities💰 Paid🚀 Launched 2024 (2y old)

📝 About this app

The Morse Decoder AI is an application for decoding Morse code signals using artificial intelligence. The application is designed for use by amateur radio enthusiasts for educational purposes. The Morse decoder listens to the audio stream through the microphone or line input and, upon detecting Morse code signals, decodes them into text.

The neural network of the application is trained to decode signals with a speed ranging from 10 to 40 words per minute within a frequency range of 200 Hz to 900 Hz.

The application supports two modes of operation: direct (default) and tone filtering mode.

In direct mode, the neural network will attempt to decipher Morse code signals in the audio range of 250 Hz to 900 Hz. This mode is suitable for confident reception of Morse code at levels of 7db-9db on the S-meter.

The tone filtering mode is ideal for decoding Morse code from noisy weak signals in the presence of radio interference. The audio input signal is first filtered using band-pass filters before being passed to the neural network for decoding. This mode allows for the decoding of faint signals, but it requires the accurate specification of the signal's tone frequency. Each radio amateur selects their own CW tone frequency in the transceiver settings, and it is important to tune precisely to the carrier frequency using the ZIN/SPOT button in YAESU transceivers, or the AUTOTUNE button in ICOM transceivers. There are 3 band-pass filter options available: 25Hz, 50Hz, 150Hz. If you can accurately determine the CW signal tone frequency, using the 25Hz filter, you can decode very faint Morse code signals.

The application offers two neural network options: A and B, which can be easily switched in the interface. Network A is recommended for use with stable signal transmission with a constant duration of dots and dashes, while network B is recommended when using a straight key, where the duration of dots and dashes may vary. You have the ability to switch between these networks in real time and observe how each network hears and decodes Morse code.

It is important to monitor the level of the incoming audio signal, for which the application provides a sound level indicator. Ensure that the signal is not too quiet or too loud. It is recommended to maintain the signal around -7db, which is sufficient for decoding. Keep in mind that higher audio frequencies are quieter than lower frequencies.

Additionally, the application provides various color themes, allowing radio enthusiasts to customize the appearance of the application for comfortable use.

🆕 What's New · v1.1

- Added Band Pass filter for decoding weak signals
- Improved neural networks type A and B

Profile & Insights

Everything we know — and don't — about this app and its company.

Identification

App name
Morse Decoder AI
Developer
Yuriy Kvasha
Bundle ID
us.kvasha.morsedecoderai
App Store URL
Open in App Store
Category
Utilities
Content rating
4+
Languages
EN

Company

Website
www.kvasha.us
Tagline
Kvasha Software
Description
Kvasha Software is a software developing company.
Founded
Not found
HQ / Address
Not found
Employees
Not found
Logo
Not found

Revenue

Verified revenue / mo
Not found
AI revenue estimate / mo
Not found
AI annual estimate
Not found
ML model estimate / mo
$86/mo
Top-grossing rank
Outside top 100 in US Utilities
All-time revenue
Not found
Pricing
Not found

Founder

Name
Not found
X / Twitter
https://twitter.com/kvashasoftware
LinkedIn
Not found
GitHub
Not found
X followers
Not found
Public statements
Not found

Funding

Total raised
Not found
Last round
Not found
Investors
Not found
Crunchbase
Not found
AngelList
Not found

Press & Links

Articles found
Not found
Listed on
Not found
Blog
Not found
Press / News
Not found

Contacts & Socials

Socials
facebook · twitter · youtube
Email
Not found
Phone
Not found
Contact page
https://www.kvasha.us/support/
About page
Not found
📈Ratings growth4 ratingsShow 3-year history estimate ▾Jan 2024Mar 2025May 2026
Tracked (4 weeks) Pre-tracking estimate (29 weeks) · model-based, ±5% noise · anchored to release date and current value
🌍Geographic ReachNot ranked
This app is currently outside the top 100 grossing in all 9 countries we monitor (US, UK, DE, FR, JP, CA, AU, BR, IN). Niche or new apps often launch this way — popularity rankings appear once daily revenue clears the regional threshold.
Profile is built from iTunes Lookup + developer site scrape + ML revenue model. Empty fields show "Not found" — additional sources (Crunchbase, X, IndieHackers, Acquire.com) coming.

More by Yuriy Kvasha

View all →

More Utilities