what to sell.
will it make money.
who is the soft incumbent.
the studio runs scheduled sweeps of the amazon catalog so you don't. you get a ranked list of niches with a one-line thesis each, an honest sales estimate (with the confidence band the model deserves), and an alert the morning your rival drops price.
- · BSR-to-units bands with a real confidence interval — never a fake promise
- · the time-series we accumulate is the moat — your one-off renter cannot replicate it
- · no raw rows, no spreadsheets to sift — you buy a decision
- · REST + MCP for outside agents · sub-200ms calls when cached
six products · one feed · shared time-series
built on the dataset. priced for the decision.
every product reads from the same scheduled-sweep history. a single snapshot is a commodity — the accumulated time-series is the product.
niche.finder
$39/mothe question every would-be seller pays jungle scout for: what should I sell. ranked sub-niches with a one-line thesis each.
- ·category sweep on demand
- ·demand vs competition scored
- ·history seeded for free
demand.track
$29/mo · 100 ASINsBSR over time is the closest public proxy for sales volume. we maintain the weekly series and convert it into a units/month band with an honest confidence.
- ·weekly time-series per ASIN
- ·category-specific BSR→sales curves
- ·low/med/high confidence on every estimate
review.mine
$19/reportthe category leader's reviews are the redesign brief. recurring complaints, 'I wish it had' patterns, exemplar quotes, severity-ranked.
- ·top-N ASINs in any niche
- ·complaints clustered by pattern
- ·the build-a-better-version spec
launch.guard
$49/mo · 25 ASINswatch your ASINs and your rivals'. price drops, stockouts you can capitalize on, BSR surges, buy-box hijacks. every six hours by default.
- ·before/after on every alert
- ·email + webhook delivery
- ·configurable thresholds per rule
arbitrage.map
$99 once · or $29/moproducts that win on .com but barely exist on .de / .co.uk / .fr. the studio scrapes six marketplaces on one schema — this product just reads the gap.
- ·any marketplace pair
- ·absent / thin / present, ranked
- ·rolling refresh available
listing.doctor
$0.25/ASIN · min $9your listing vs the category top performers, dimension by dimension. title, bullets, images, price position, rating. prioritized fix list.
- ·category median benchmark
- ·severity-ranked findings
- ·concrete fix per finding
how it works
the spine is the same for every product.
- 01
give us a category URL or an ASIN list.
you don't write SQL, you don't sift rows. you state the input and pay for the answer.
- 02
the agent sweeps. the wrapper enforces.
every Amazon Scout call is capped at the boundary — max products, max URLs, every call logged. a hallucinated 200,000-product sweep is impossible, not just discouraged.
- 03
the model runs on accumulated history.
BSR→sales, demand vs competition, review-gap synthesis — all computed against the shared time-series we've been collecting, never from a single snapshot.
- 04
you get a decision.
ranked opportunities with a one-line thesis. velocity bands with a confidence interval. a redesign spec with exemplar quotes. an alert with the before and the after.
REST · MCP
for the agents you're building.
every product surface is also a REST endpoint and an MCP tool. bearer-token auth, the same prices as the browser surface, deterministic guardrails at the wrapper boundary so your agent can't accidentally invoice you for a 200,000-product sweep.
// niche.finder — REST
POST /api/agents/niche-finder
authorization: Bearer si_xxx...
content-type: application/json
{
"category_url": "https://www.amazon.com/Best-Sellers-Pet-Supplies/zgbs/pet-supplies",
"marketplace": "com",
"max_products": 120
}
→ 200 OK
{
"runId": "0b2c...",
"opportunities": [
{
"sub_niche": "Cat Trees",
"demand_score": 71,
"competition_score": 38,
"thesis": "the category leader sits at 4.1 with 612 reviews; BSR climbing — beatable.",
"top_asins": ["B0X1...","B0Y3..."]
}
]
}questions
honest answers.
- isn't BSR-to-sales a guess?
- it's a model, and we say so. every estimate carries a low/med/high confidence band derived from how many history points we have and how volatile the BSR has been in our window. we never launder a model into a promise. "estimated 800–1,400 units/month, medium confidence — BSR is volatile in this category" is correct. "guaranteed 1,200 sales/month" is wrong.
- where does the data come from?
- amazon-scout.0p.studio — a sibling product the studio runs. every seller-intel product consumes it through its public MCP server, the same way an outside developer would. amazon scout sells the rows; seller intel sells the judgment.
- what's the moat?
- the accumulated time-series. a single snapshot is a commodity anyone can buy. a maintained history of BSR, price, and review-velocity deltas across six marketplaces is something only a scheduled-scrape operation can assemble. we scrape the category once per cadence and amortize across every customer interested in that category.
- do I have to learn an API?
- no — the browser surface is the primary one. but every product is also a REST endpoint and an MCP tool, and the price is the same. agents are first-class customers.
- can I cancel?
- any time, from the billing page. failed payments don't delete data; per the studio's policy, the lights stay on.
- is this affiliated with amazon?
- no. amazon is the data source. there is no endorsement, no API key, no partnership, and we don't use any of amazon's marks.