Methodology
A research desk, not a prediction machine.
TrendSeer turns fragmented ecommerce signals into a bounded product-risk brief. The output is designed to help a small brand decide what to test, what to watch, and when to stop.
Core chain
Signal to Trend to Assessment to First test
The chain keeps the product honest: sources inform the trend, the trend informs the assessment, and the assessment informs only a bounded action.
01 / Source signals
Start with traceable evidence
TrendSeer separates visible marketplace examples, merchant input, manual snapshots, and simulation output so a weak source cannot become a strong claim.
02 / Trend object
Turn fragments into one decision object
The brief names the product angle, comparable offers, missing facts, and the first buyer question worth testing.
03 / Pressure test
Use MiroFish to find failure modes
MiroFish can cap or downgrade the recommendation by surfacing trust risk, channel friction, copyability, or claim risk. It cannot upgrade weak evidence.
04 / First test
End with a bounded action
Every brief ends with a first test, success signals, and stop-loss rules so a founder knows what to do before spending more.
What counts as evidence?
Public marketplace examples, competitor pages, merchant-provided URLs, manual screenshots, review snippets, and first-party test data can support a brief. Simulations and ROI models pressure-test the action, but they do not create market truth by themselves.
Why manual review first?
Early briefs are manually reviewed because small stores need judgment, not raw automation. The repeatable parts become productized only after the service proves that founders will pay for the decision quality.
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