console · runtime intelligence for your team
Ask your catalog anything.
One assistant answers.
Your merchandising, pricing and design teams ask their own catalogue questions in plain language — and one assistant answers, with the receipt attached. No report queue. No dashboard to learn.
883 products this catalogue can style more than one way — its strongest anchors. Another 2,434 can be styled exactly one way. No product here anchors more than two looks of its own — so those 883 are as deep as this range gets.
measured · full count of the complete-the-look set, on the 4,999 products we publishThe console
No one has to learn this screen.
They just ask it.
The screen below is real — we built it and we run it. We rebuilt it here, screen for screen. Every figure in it is one this page has already counted in front of you — nothing is invented. One voice answers; a fleet prepares behind it — and what it prepares lands back in the same thread, then it hands the work to a person.
Normally these two states disappear the moment the answer arrives: the wait indicator, and the locked box that stops you mixing two questions in one thread. We froze them here so you can see what the wait actually looks like.
Your assortment health — breadth, depth, newness and coverage across your own catalogue.
- Not this setup. Read, and this configuration doesn’t build looks around that class.
- Never came up. Readable, allowed — never reached for.
- Couldn’t read it. The photo didn’t give it enough of the garment.
- No image. Nothing to read.
Nothing lands in a gap unlabelled. The label is what routes the work: a setting to change, a piece to buy around, or a photograph to re-shoot.
measured · full count, no samplingThe 883 two-look anchors are the range’s strongest pieces; the 2,434 one-look products are one garment away from a second outfit. A further 18 appear only inside others’ looks, anchoring none of their own.
measured · same count as the answer aboveYour price position — where the bands sit across your own catalogue.
One band does the heavy lifting: $50–75 holds 1,578 pieces — roughly a third of the range — while the premium tail past $150 thins to a handful. No original compare-at price is recorded, so this is the live price the range actually trades at, with no markdown layer on top. That concentration is the decision: widen the thin premium end, or defend the crowded core.
measured · every product’s current price, counted — no samplingTrend is real and it is in the console — you can see its tab. It stays shut on this page: its screen benchmarks against competitors, and we show nothing here we haven’t measured on our own demo catalog. It carries one caveat, and we’d rather print it here: a benchmark to weigh, not a forecast to trust.
bunsarthe console, rebuilt our own demo catalogan illustration — not a screenshot, not a session
One room for the whole team
Everyone asks the same catalogue — in their own words.
Merchandising, pricing and design don’t each file a request and wait on a report. They open one assistant and ask, the way they’d ask a colleague who has read the whole range.
- Plain language in — no query to write, no dashboard to learn
- Answers on your own catalogue, not a generic benchmark
- Ask in English or Turkish; the assistant reads either
- The same room for every team, so the numbers agree
products this catalogue can style exactly one way — each one a single garment away from a second outfit. The other 883 style more than one way, and two is the ceiling here.
measured · full count of the complete-the-look set, on the 4,999 products we publishThe receipt is attached
Every answer names what it measured.
No answer arrives as a bare number you have to take on faith. Each one carries a one-line receipt — the population it counted, and whether the figure was measured directly or derived from a smaller set — so a merchandiser can trust it and an analyst can check it.
- Each figure tagged measured or derived, in place
- The exact population named — 4,999 products, not ‘the catalogue’
- No sampling hidden behind a round number
- A piece it can’t dress comes back named, with the reason — not a percentage
Honest about its edges
When it can’t answer yet, it says so — then prepares it.
A question the assistant hasn’t already worked out doesn’t get a confident guess. It tells you the count doesn’t exist yet, puts the work in hand, and the answer lands back in the same conversation when it’s ready — so you’re never told something that only sounds right.
- No bluffing — an unmet question is named, not faked
- The conversation waits; the answer returns to the same thread
- You always know whether you’re reading a settled fact or a fresh one
This preparing state is the visible half of the loop — kept on screen here so you can see it; in the console it clears itself the moment the answer is ready.
How it works
One voice in front. A fleet behind it.
- You ask one assistant. Fashion Intelligence reads the question the way a colleague would — in plain language, in your own words, in English or Turkish.
- A fleet prepares the work. Behind that one voice, specialists do the measuring and hand it forward. You never meet them: one assistant in your console, one workshop behind the wall.
- The work is handed to a person. When something needs fresh preparation, the fleet prepares it offline and the finished answer lands back in your thread — a person decides what to do with it.
The honest line
One brain answers your team.
One voice answers; a fleet prepares the work behind it — and then it hands the work to a person. The assistant measures and explains; it never reaches into your systems and never acts on your catalogue on its own. What it prepares, a human puts to use.
Where the taste comes from
Learned from real, well-dressed people.
Bunsar’s styling intelligence is learned from real, well-dressed people — a continuously refreshed feed of public outfits, curated per market and occasion — not synthetic data. The looks on this page are AI-rendered from real catalog garments; the taste behind them is real.
AI image — garments are real catalog products
AI image — garments are real catalog products
AI image — garments are real catalog productsThe proof is the answers.
No borrowed benchmarks live on this page — no lift figure, no session count, no logo wall. The proof is the work the console did: three real questions, answered on our own demo catalog, each answer naming what it counted and whether the figure was measured or derived.
every one of the 4,999 products carries a live price, 239 distinct points concentrated in a $50–75 core with a thin tail to $258 — the price the range trades at, no markdown layer on top.
measured · every product that carries a price, countedof the sixteen to twenty it can show for a single product, served from pieces near it. The rest exist and it can name every piece — they just have no photograph yet.
derived · five anchors, recomputed — not a catalogue-wide averageways a piece ends up with no look — and the engine names which one it hit, for every piece: a class this setup doesn’t dress, a piece it never reached for, a photo it couldn’t read, or no photo at all. Which one it is tells you whose job it is — a setting, a buy, or a shot.
measured · our own demo catalog, full count, no sampling — every undressed piece bucketed by the outcome that stopped itTrend · visible in the console, held shut here
Trend is real and it is in the console — you can see its tab. It stays shut on this page: its screen benchmarks against competitors, and we show nothing here we haven’t measured on our own demo catalogue. The one caveat it ships with is the point: a benchmark to weigh, not a forecast to trust.
And the same assistant is honest about what it cannot dress — when it can’t build a look around a piece, it says what stopped it, by name.
Put your team on it.
Point the Console at your own catalogue and let merchandising, pricing and design ask it directly — each answer with the receipt attached. We’ll set up a demo on your products.
Book a demo