// AI-ASSISTED PROJECT REVIEW
Code Review for AI-Built
Audio Software
AI lets anyone turn an audio idea into a working prototype with Claude, ChatGPT, or Cursor. Turning that prototype into a commercial product takes professional engineering. Our senior C++ and JUCE developers audit your code, send you a clear proposal, and can then implement the fixes through to release.
// FROM PROTOTYPE TO PRODUCT
AI Drafts the Idea. A Commercial Release Takes Engineering.
From idea to prototype, without writing code
AI tools like Claude, ChatGPT, and Cursor now let musicians, producers, and founders without a technical background turn an idea into a working plugin or audio app in days. That is a real opportunity: you can test a concept, hear it, and show it to people before investing in full development.
A prototype is not a commercial release
Selling software to thousands of users on different computers, operating systems, and DAWs is a different challenge. Real-time audio leaves almost no margin for error, and AI-generated code tends to hide problems that only appear under real-world use: thousands of crash reports, audio dropouts, lost presets, and behaviour you never saw in your own tests. Fixing those after launch costs far more than catching them before.
Why deep experience matters
Shipping a stable audio product takes deep knowledge of DSP and real-time programming, experience with how every host actually behaves, and professional development practices for testing, signing, and releasing. Asking the same AI to review its own output rarely finds these issues. Our team builds and maintains commercial audio products for companies like Audiomodern, DiscoDSP, CodWaves, and LatencyFix.
That is why we offer the audit
We review your AI-built project the way we review production code, show you exactly what stands between your prototype and a commercial release, and give you a clear plan to get there.
Request an Audit// WHAT WE REVIEW
What Our Audio Code Audit Covers
Real-Time Safety
We find memory allocation, mutexes, logging, and file or network access on the audio thread. AI tools produce these constantly, and they surface as clicks, dropouts, and crashes only under real sessions.
DSP Correctness
Filter stability, denormals, NaN and Inf handling, aliasing, parameter smoothing, and sample-rate or buffer-size changes. We check that the processing is right, not just that it makes sound.
Host & Format Behaviour
State and preset recall, automation, latency reporting, and thread safety between editor and processor. The issues that pass a quick test in one DAW and fail in Pro Tools, Logic, or Ableton.
Architecture & Maintainability
Whether the codebase can grow. We flag duplicated logic, tangled ownership, and fragile structure that will make every new AI-generated feature harder to add without breaking something else.
Performance & CPU
Hot paths, needless copies, per-sample virtual calls, and inefficient buffers. We tell you where the CPU goes and which optimizations are worth doing before release.
Release Readiness
Build setup, code signing, macOS notarization, installers, pluginval and format validation. Everything between a working prototype and a product customers can install.
// HOW IT WORKS
Audit, Proposal, Implementation
Every engagement starts with an audit and a proposal, so you know exactly what is wrong and what it takes to fix before any implementation work begins.
Share your project
Tell us what it does, which AI tools you used, and what worries you. Then give us access to the code. NDA available from the start.
Audit
Senior developers build your project, run it in real hosts, and read the code for real-time safety, DSP, host behaviour, architecture, and release readiness.
Proposal
You receive prioritized findings in plain language and a scoped plan for making the product stable and release-ready. We walk you through it.
Implementation
Once you approve the proposal, we implement the fixes, harden the codebase, and take your product through testing and release.
// FAQ
Common Questions
Can you review a plugin that was mostly written by AI?
Yes. That is exactly what this service is for. Whether the code came from Claude, ChatGPT, Cursor, Copilot, or a mix of AI and hand-written work, our senior audio developers read it the same way we would review code from a new team member and tell you what is sound, what is risky, and what needs to change.
What do you look for in AI-generated audio code?
Real-time safety on the audio thread (memory allocation, locks, logging, file access), DSP correctness, parameter smoothing, sample-rate and buffer-size handling, state and preset recall, threading between the UI and audio engine, host compatibility, and whether the architecture can be maintained and extended.
What do I receive after the audit?
A prioritized written report that separates critical issues from nice-to-haves and explains why each one matters in plain language, together with a proposal: a scoped plan of the work needed to make the product stable and release-ready. We walk you through both so you can decide how to proceed.
Do you only review JUCE plugins?
No. We review VST3, AU, and AAX plugins, standalone desktop audio applications, mobile audio apps, and audio machine learning code. JUCE and C++ are our core, but we also review Python ML pipelines and other real-time audio stacks.
Is my code kept confidential?
Yes. We work under NDA as standard and your code and product ideas remain confidential.
Can you implement the fixes after the audit?
Yes. That is the natural next step. Once you approve the proposal, our team implements the fixes, hardens the codebase, and takes the product through testing and release. If you prefer to do the work yourself or keep iterating with AI tools, the audit report is yours to use.
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