We've had people tell us Clarity changed how they train, how they work, and how they sleep.
Most interestingly, people have told us that they are in fact drinking more caffeine, safe in the fact that they understand how the dose is being processed in their body and how it could be impacting their sleep if not timed correctly.
This isn't we invented anything. Because we put the science somewhere people actually use it - on their phone, in their routine, before the decision gets made.
I've always been on the personal brand side. More and more founders are building it now (sometimes even before the product is ready while it's still in development, before seed fundraising). The CEO builds their position so the product sells more easily at the official launch.
But I have experience with people who built the product, scaled it, and only then did we discover who was behind it.
Honestly, with the first approach, I'd be concerned that people invest more in me as a person than in the product. People would idealise the founder and overlook the product's flaws (which could hurt development and constructive feedback).
+ I noticed the most common mistake that many people who started building a personal brand first, connected their product to their personal accounts (emails, social media, etc.) and started having a problem selling these things, because they cannot "give someone keys" to their personal profiles.
I have been thinking about this a lot lately: why do so many AI products feel interchangeable?
You open one, you open another. Different logo, different color scheme, same experience. A text box. A chat interface. Some version of "ask me anything." The wrapper changes but the feeling does not.
Lately, I ve been looking closely at how independent builders and small teams are managing AI knowledge bases. It feels like the default "industry standard" is to immediately reach for a complex RAG pipeline and a heavy, paid Vector Database.
But I'm starting to wonder if we are over-engineering this for 90% of standard use cases.
Vector DBs are incredibly powerful for massive scale, but for smaller or non-massive datasets, they can be expensive, complex to query, and act as complete black boxes. If a search returns a weird chunk, diagnosing it is often a nightmare.
