Ozone 12
Leading design for the next generation of an industry-standard audio mastering tool used by pros.
Target Audience
Audio mastering engineers
Environment
DAW-based workflows
Role
Lead Designer
Timeline
End to end product design
What is Ozone?
A complex, professional mastering suite
Ozone is a professional mastering suite that combines multiple signal processing modules into a single, real-time workflow. It’s a dense, parameter-heavy environment where speed, clarity, and precision are critical.
The challenge
Fragmentation and inconsistency
Within iZotope Ozone 12, the interface had become fragmented, with inconsistent components and one-off patterns that increased cognitive load and made the system harder to scale. This lack of cohesion also slowed development and limited our ability to evolve the product efficiently.
Lack of trust in assistive tech
At the same time, trust in Master Assistant was eroding. Its processing felt opaque, limiting user control and making it difficult for engineers to confidently refine results.
Working with poor mixes
These challenges were amplified in real-world mastering scenarios, where users often work with poor mixes that are difficult, and sometimes impossible, to fully resolve.
Risks and considerations
Preserving trust while evolving the system
As one of iZotope's highest-performing products, Ozone carried significant legacy trust with its user base. The risk wasn't just breaking workflows. It was breaking the mental model engineers had built around the tool over years. I made a deliberate call early on: we would not redesign for redesign's sake. Every change had to directly resolve a documented pain point or improve scalability. Where the tradeoff was unclear, I defaulted to preserving the familiar pattern and flagging it for a future iteration.
From Automation to Collaboration
Framing
In our research, users frequently reported that fully automated results felt impersonal and often overstepped without sufficient context. After several generations of improvements to the neural nets and DSP, it was clear that the answer wasn’t a better black box. We needed to improve how users interact with the assistants.
Solution
I introduced a guided pre-processing step that lets users shape intent before running the assistant, shifting from a one-click outcome to a more collaborative workflow. Internally, we framed this as a “just add eggs” approach, giving users meaningful input to increase control and ownership while still leveraging intelligent processing.
1
Users can select their target ahead to augment the applied processing
2
A simple list and a toggle function enables users to quickly select which modules they want the assistant to consider using
3
Intensity and loudness controls help set the desired level of transparency with the processing and the target output loudness
Outcome
In Ozone 12, Master Assistant became something users could shape, not just run, with post-launch surveys showing improved trust and usability.
Designing for Imperfect Inputs
Framing
Mastering engineers are often forced to work with imperfect mixes, where tonal imbalance, over-compression, or low-end issues limit what can be achieved downstream.
Solution
We introduced a set of targeted processing tools designed to make imperfect mixes more workable within the mastering stage. These tools address common upstream issues directly, giving engineers control over tonal balance, dynamics, and low end response without breaking their workflow.
1 - Stem EQ
Enables adjustment of tonal balance and gain across individual mix elements from a single stereo file, allowing for more precise corrections without access to stems.
2 - Unlimiter
Restores transients and dynamic range in overly compressed or limited mixes, recovering impact and clarity that would otherwise be lost.
3 - Bass Control
Provides targeted control over low end strength and presence, helping balance muddiness or lack of weight in drums and bass.
Outcome
These tools expanded what engineers can realistically address during mastering, reducing reliance on external fixes and time-consuming back-and-forth. By making imperfect mixes more workable, they enabled faster, more flexible workflows and improved confidence in the final result.
Building a Scalable Visual System
Framing
As Ozone evolved through the years, incremental UI decisions led to a proliferation of component variations, limiting consistency and making the system harder to scale and maintain.
Solution
This work focused on reducing redundancy and unifying component behavior, consolidating variations into a smaller set of flexible components that work across light and dark contexts. Hover states were standardized, and inconsistent skeuomorphic elements were removed in favor of a more cohesive digital aesthetic, creating a more predictable and scalable system.
1
The interface had accumulated a proliferation of small variations and one-off component patterns, reducing consistency and making the system harder to scale and maintain.
2
Components were consolidated into a smaller, more flexible set that work consistently across both light and dark contexts, reducing redundancy and improving predictability.
3
The use of light, mid, and dark values was standardized to create more intentional contrast and a clearer, more consistent visual hierarchy across the interface.
Outcome
This improved visual consistency and reduced cognitive load, while increasing development efficiency and enabling the system to scale more effectively across future releases and plugins. Component variance was reduced by 40%, simplifying both design and implementation.
Impact
Driving Product Evolution
This work strengthened Ozone 12 and future releases by improving trust in intelligent features, expanding what can be addressed during mastering, and establishing a more scalable system foundation. It defined a repeatable approach for designing complex, real-time tools where precision and usability must coexist, translating advanced DSP into intuitive, controllable workflows while reducing system complexity through consistent patterns and shared components.