Grocery, fashion, electronics, pharmacy — the words change, but the problem is identical: a real person says something a normal way, and the platform doesn't get it. Here's what that actually looks like, category by category.
Someone says "dommala bat" at 9pm and means a mosquito racket, not a cricket bat. Regional names, quantities, basket orders — all in one breath.
"Ganusugaddalu" is sweet potato in Telugu. Nobody's going to type the English name for a vegetable they've called one thing their whole life.
"Laptop bag with a shoe compartment" isn't in any product title — it's buried in a description three clicks deep, if it's findable at all.
"Something for my mother's saree, cotton, under 1500" is three requirements in one sentence — occasion, material, budget — not a keyword.
A farmer describes a pest problem, not a product SKU. The search has to work backwards from the problem to the right spray.
A shopkeeper says "how much rice left" out loud while both hands are busy — the software should just understand that.
Someone searches a symptom, not a drug name. This one needs real care — Sequena surfaces options, it never diagnoses.
"Something light, not too spicy, quick" is a completely normal way to order food — and completely unsearchable on most menus today.
Commerce is where we're proving this first. The same understanding generalizes to any place people say what they want out loud and expect to be understood — that's the longer story, not today's pitch.
Our 199-query benchmark splits almost evenly across two very different shopping experiences — because "does this generalize?" is a fair question, and the only honest answer is to actually check.
Mostly regional grocery names and quantities getting misread — a different flavor of failure than fashion search has.
Mostly occasion, colour and native-script names going unrecognized — different words, same underlying gap.
To be clear: these numbers are how the existing platforms performed on our test set — not a claim about Sequena's own accuracy. Measuring that against this same corpus is the next thing we do.
Bring your own queries. We run them, show you the before and after side by side, and let the numbers make the case — not a slide deck.
The jargon terms behind these are standard search-quality metrics — the short version is: did it find the right thing, and near the top?