Start small if you want. The front-door offer is a 48-hour teardown that tells you whether there is a real trust problem and whether a deeper review is worth it.
Send the product URL and an optional concern. I will tell you whether this looks like a teardown, a full review, or not a fit.
I reply with fit, the smallest useful starting point, and whether this looks like a teardown, full review, or fix sprint.
A low-risk first step for founders who want to know quickly whether privacy or trust risk is real.
The flagship offer when you need a serious review of how your product collects, stores, and handles sensitive user reality.
Implementation support for the highest-value corrections once the trust failures are already known.
Final scope depends on product surface area, access available, and whether the ask is diagnostic, roadmap-focused, or implementation-heavy. The listed prices are starting points so buyers do not have to guess whether the work is accessible.
This is for founders with a live or near-launch health app who need a hard look at where the product collects too much, assumes too much, or quietly routes intimate user data through the wrong systems.
A pre-launch privacy audit is useful when the product is nearly ready, but nobody has yet forced the system to justify its collection paths, recovery behavior, and trust claims under real conditions.
If an AI product touches health, legal, workplace, or other sensitive user reality, the risk is usually not the model alone. It is the surrounding product surface: logging, retention, prompts, exports, fallback states, and claims the team cannot actually defend.
This is for teams building health or wellness products that should stay useful under low trust, low attention, or partial connectivity, but still need a practical architecture review before launch.
If the uneasy feeling is that the product collects too much, logs too much, or keeps too much by default, this review turns that vague concern into a clear minimization plan before launch makes the problem more expensive.
Wellness products often look low-stakes until they begin collecting intimate patterns, habits, symptoms, or relationship data. This review is for teams that want those boundaries fixed before trust debt piles up.
Mental health products create trust risk quickly because user context is often fragile, low-energy, and high-consequence. This review focuses on the product decisions most likely to break trust before users or clinicians do.
This is for teams that know a generic penetration test is not the whole answer. The goal is to find the launch-relevant security and trust failures inside the product model itself, not only perimeter weaknesses.
When a product collects more than it needs, every other trust discussion gets harder. This review is for founders who want a practical minimization pass that reduces risk without flattening the product into nothing.
Some teams do not need a theory discussion. They need to know whether the product is about to launch with silent trust failures that will become expensive once users, buyers, or partners begin inspecting it.
Send the product URL and, if useful, the main concern. Add a deadline only if timing matters. I'll reply with fit, likely package, and next step.