Insights, playbooks, and product thinking for Amazon sellers who treat their business like a business.
Every tool in the Amazon SaaS ecosystem claims to use AI, but almost none train the models they say they trained. Under the word sit three very different layers — rule-based logic, real machine learning, and LLM APIs called at inference time — and most 'trained on Amazon data' claims are prompt engineering plus retrieval. This is legitimate work; it just isn't what the marketing describes.
Every productivity tool promises to save time, but Parkinson's Law and the Jevons Paradox suggest the hours never come back — they get absorbed by more work, more expectations, more scope. We were never really selling time. We were selling attention, confidence, and reduced cognitive load. The most useful software of the next decade will stop promising hours back and start protecting the attention we can actually keep.
MCP is a real, useful protocol — but the dashboard-is-dead, AI-replaces-everything narrative depends on something most products don't have: a coherent data foundation. Without it, MCP just makes hallucinations faster. The companies that win the AI-native future are the ones doing the unglamorous work of rebuilding their data foundation now, while everyone else demos chatbots.
After six years managing tech for Amazon brands, we built Clarisix because the analytics industry is broken: fragmented tools, unreliable data, and executives reduced to a Monday morning ritual of CSV-stitching. Clarisix unifies Sales, Advertising, Profitability, Inventory, Content, and Customer Experience into one five-minute weekly scan — built for executives, not analysts.
We're starting the Clarisix blog to share what we're learning while building the analytics platform Amazon sellers actually want. Expect playbooks, opinionated takes on the six pillars, and the occasional product update.