Work / SereniBrain
The hardware is finished. The app is the opportunity.
SereniBrain ships a brain-sensing meditation wearable with a companion app that records everything and reveals almost nothing. This concept redesign turns the data the device already captures into the product's real value, and its strongest, lowest-cost growth lever.
01 The problem
SereniBrain's engineering gravity sits where you'd expect for a neurotech startup: the EEG headband. Dry electrodes, signal processing, brainwave classification. That is the moat, and it is genuinely good.
The companion app, by contrast, behaves like a data read-out for the hardware rather than a product in its own right.
Every one of those questions spans many sessions. The app only ever shows one.
02 Context of use
I grounded the redesign in three months of continuous practice (59 device-recorded sits plus a daily reflection journal) and, just as importantly, in the situation those sits happen in. Focusing the context of use, not just the numbers, reframed the whole product.
Average calm by day of week, reconstructed from the export
Thursdays are tough. Depth varies sharply by day, a pattern that is invisible in a session-at-a-time view.
03 The reframe
Before the redesign the app is a session viewer: it grades the last sit and waits to be exported. After it's a practice coach that remembers, compares, and tells you what to do next, in your own context.
No new sensors. Compute settle-time, trends, and best-window from data already logged. The lowest-lift, highest-value move in the product.
"52% calm" is noise. "Above your 42% average" is feedback. Every number is framed against the user's own history.
Lead with time-to-settle, the metric tied to the user's actual goal (deep absorption, faster), not an opaque 0 to 100 score.
A two-tap pre-sit check-in closes the loop the journal was already trying to close, and makes every later insight sharper.
04 The redesign
Insights · the cross-session view
A time-to-settle trend, a best-window heatmap, and plain-language cards: arriving alert is your number-one driver, Thursdays are tough, your sweet spot is about 20 minutes.
Session report · from grade to feedback
The old screen delivered a bare "33 · Poor" and a donut. The redesign opens with a plain-language headline, then three numbers that each compare to the user's baseline, and ends with a one-line reflection prompt.
Pre-sit check-in · the one new input
Before starting: how are you arriving (alert, neutral, sleepy) and which practice. This is the only added data capture in the whole redesign, and the most valuable, because arrival state predicted depth better than any sensor signal.
05 The business case
Hardware is a one-time sale with thin margins and a returns risk. The app is where a neurotech brand earns recurring revenue, retention, and word of mouth. SereniBrain is under-investing in exactly the layer that compounds. The redesign is attractive precisely because the hard part is already done.
Single-session feedback stays free; trends, best-window, forecasts, weekly digests, and unlimited history become a low-cost subscription. The asset already exists in the export, it just isn't packaged or priced.
A coach that gives you a reason to open the app before every sit, plus a streak and forecast to protect, lifts session frequency and cuts the churn wearables face after the novelty fades.
"My brain settles 40% faster on Fridays" is screenshot-worthy in a way a donut is not. Personal-progress cards turn users into a marketing channel for the hardware.
When the band visibly makes you better over time, the purchase feels justified. Insight reduces buyer's remorse, protecting the core hardware revenue directly.
Every feature here is computed from data already collected. No new hardware, no new sensors, no ML retraining. The lift is design surface area, not R&D, which puts the whole redesign in the rare low-effort, high-impact quadrant.
Note: the revenue and retention framing above is stated as opportunity hypotheses to be validated, not measured results from a shipped product.
06 The hard parts
The redesign looks tidy in hindsight. However, most of the actual work happened upstream, in the unglamorous business of getting the data into a usable state and then figuring out what to ask of it.
The single hardest part of the project was consolidating a long trail of separate files by hand. My journal was worse: transcribing months of handwritten entries that existed only on paper for essential arrival state context data.
My moveI did the manual work, transcribing the journal, merging everything into one place, and deciding entry by entry what was signal and what could be omitted. Then I checked for duplicates and entry errors.
Clean data is not the same as insight. I knew ideas weren't going to leap out of those numbers from staring at them. I had to cut out the filler and decide what to disregard.
My moveI went back to why I meditate in the first place. Luckily I record annual goals for each area of my life, and could reference them to build a list of what actually mattered. I then framed the analysis around these questions: does consistency matter more than session length, do longer sits create deeper calm or just more fluctuation, am I settling faster than I was in February, what is my minimum effective dose, and what conditions surrounded my best sessions?
Asking a model to "analyze this data" hands it the job of deciding what matters. It will surface what it considers important, which is a fine starting point on some projects but it was the wrong one here, where the whole point was meeting a specific personal goal.
My moveBefore using AI on anything, I define the goal and set the parameters so it enhances my analysis instead of replacing it. I fed it my actual questions and the context behind them, asked it to surface recurring themes and trends, and required it to cite where each conclusion came from so I could check if anything sounded off. An AI output is only as good as the precision you bring, which is why I save this kind of work for when I have the mental energy to be exact.
Then came the moment of truth: now that I had the patterns, what do I do with them? The first dashboards the analysis produced were not good, which was clarifying. It told me this needed to be a designed, visual thing rather than a report.
My moveI started where all my designs start, with a rough pencil sketch in my planner. Then I got specific about the ask, requesting practical improvements that would be a "low architecture lift" and would help the user identify patterns, reflections, and actionable insights. I pointed to an app I admired for reference, and fed in my sketches alongside what the current app actually looks like. The more context I gave, the better the output came back.
A mockup shows what could be built. It does not explain why a hardware company should spend anything on it: the argument that decides whether a redesign ever happens.
My moveI pushed past the interface and into the business case: the hardware here is genuinely good, but the software is where a neurotech brand earns recurring revenue, retention, and word of mouth. Framing what the company is leaving on the table by under-investing in the layer that compounds is what turns a concept into a proposal.
Every one of these was a judgment call rather than a technical step, and each one shaped the product more than the visual design did.
06 Reflection
The most useful move in this project wasn't analysing brainwaves. It was asking who is reading this, when, and in what state. The same dataset that produces an intimidating 400 MB export becomes a calm, two-line morning nudge once you design for the moment instead of the metric.
If I took this further
What I'd carry forward
Good data is necessary. Knowing the situation it lands in is what makes it useful.
Concept · self-initiated · 2026