We have spent almost a decade refining a single coherent approach to digital product engineering. The advantages are not features — they are the substrate of how we think, build, and ship.
Our privacy stance is not marketing copy. It is implemented at the architectural level, audited quarterly, and respected by every team member.
All user-generated data is stored on the device by default. Cloud sync is opt-in, end-to-end encrypted, and never required for the product to function.
For any data that does leave the device, we apply zero-knowledge encryption. The server cannot read the user data; only the user device can.
We do not ship Firebase Analytics, Mixpanel, Amplitude, or any other surveillance-style analytics SDK. Behavioural insights are derived from locally aggregated counters.
Our compliance engine detects the user region from the device locale and adapts the privacy notice, the consent layer, the data flows, and the storage location accordingly.
Twelve full-time engineers, designers, and researchers, with a deliberate cap at twenty-five. We grow slowly, vet carefully, and stay small enough to know each other by first name.
12 years across procedural audio, on-device ML, and large-scale privacy engineering. Ex-Spotify, ex-Apple, ex-Mozilla.
9 years of native iOS and watchOS, with deep SwiftUI expertise. Three Apple Design Award nominations.
10 years of Android, including 3 years leading the Android team at a major fintech. Compose expert since the alpha.
Former EU data-protection authority counsel. CIPP/E, CIPP/US certified. Author of two open-source privacy toolkits.
PhD in audio signal processing. Author of three JUCE modules. Specialises in procedural synthesis and spatial audio.
11 years in product design for B2C software. Former design lead at two Y-Combinator alumni. Advocates for accessibility.
8 years of applied ML, specialising in model compression for edge deployment. Core ML and TFLite expert.
Award-winning visual designer. Typography obsessive. Crafted the brand systems for four of our published apps.
Surface-level engineering is everywhere. Real differentiation lives in the depths — where a 200 ms becomes a 50 ms, where a privacy claim becomes a zero-knowledge protocol, where a UI becomes a language.
Core ML, TensorFlow Lite, and ONNX Runtime models that run entirely on the user device. Typical model size: 1–8 MB. Typical inference latency: <12 ms on A14 Bionic.
L-system plant rendering, Markov-chain text synthesis, fractal landscape generation, Perlin-noise audio textures, and genetic-algorithm UI theming.
For users who opt in, we provide end-to-end encrypted sync via iCloud Drive and Google Drive. The server never sees plaintext.
Instruments, Perfetto, and custom traces drive every UI to 60 Hz and every launch to under 1.2 seconds on a 3-year-old device. Median p95 frame time: 8.3 ms.
Full VoiceOver / TalkBack support, dynamic-type, high-contrast, and reduced-motion respect. Tested with real assistive-tech users before every major release.
Every product begins with 20+ unstructured user interviews. We do not run surveys; we sit with people for an hour and watch them work.
We do not chase awards, but the industry has been kind. Here is a selection of public recognition our products and the studio have received since 2021.
TrailMark, Inkdrop, ReceiptVault, and Mindspace have been featured in the App Store Editor Choice list in the US, Germany, Japan, and Brazil.
CurioVault was named one of the top 10 best apps for Personal Growth in the Google Play Best of 2025 awards.
Our open-source privacy-preserving analytics library, Confetti, received the Mozilla Open Source Award in the Privacy category in 2024.
Our secure development lifecycle, incident response, and access controls are ISO/IEC 27001 certified, audited annually.
Our marketing site and the TrailMark microsite received Awwwards Site of the Day in 2024 and 2025.
Recognised by the European Commission as an early adopter of the Digital Services Act transparency framework in 2026.
We will gladly walk you through a live product, an internal code review, or a privacy architecture review for a product you are already shipping.
