Sequena sits between how people actually communicate — in Telugu, Hindi, Tanglish, by voice, mid-sentence, with local words a catalog was never indexed on — and how applications search, retrieve and act. We don't own the store. We own the understanding. Commerce is where we're proving it first.
"Sequena is an intent intelligence layer that transforms natural human language, voice and context into structured, application-ready intent — so existing applications can understand, retrieve, rank and act on what people actually mean."
Netflix doesn't own theatres. Uber doesn't own taxis. Sequena doesn't need to own your catalog, your inventory, or your storefront — it owns the understanding between what someone means and what your application can act on.
These are real searches, on real Indian commerce apps, exactly as people typed or said them. Every example below is drawn from our documented test corpus — not illustrations.
Left column: measured results from live platform testing. Right column: Sequena's intent resolution, shown as intended system behaviour — end-to-end retrieval accuracy against this full corpus is currently being measured and is not claimed here.
The pipeline is shared across every application it sits in front of — grocery, fashion, electronics, food, merchant tools. What changes per domain is the ontology and constraints layered on top, not the core engine.
One shared intent schema and evaluation framework underneath — not a separate model per domain.
The intelligence generalizes — only the ontology and attribute space change per category.
Regional product names, quantity and unit extraction, basket intent.
Local vocabulary, pack sizes, festival and occasion baskets.
Long-tail discovery, use-case queries, spec and budget constraints.
Occasion, fit, colour and audience intent in native expression.
Crop, season and problem-to-product mapping in farmer vocabulary.
Voice-to-business-action for kirana and neighbourhood commerce.
Symptom-to-product inference with strict safety boundaries.
Commerce is the proving ground. The same intent layer is built to generalize to any application where humans express intent and software has to act on it — that's the longer arc, not the current pitch.
Drop it in front of your existing search, catalog or workflow. No re-indexing, no rip-and-replace — you keep the catalog, the inventory, the transaction. We hand you the understanding.
{
"input": "5 kilo kandi pappu kavali",
"modality": "text",
"domain": "grocery"
}
{
"language": "te-en",
"intent": "product_search",
"entity": "Toor Dal",
"quantity": { "value": 5, "unit": "kg" },
"attributes": {},
"constraints": {},
"normalized_query": "toor dal 5kg"
}
Schema shown is illustrative and expected to evolve. /understand is the core endpoint; /search-intent, /extract and /rank are on the roadmap once the pilot proves the core contract.
If a person can say it, your product should understand it — in search, ordering, discovery, or support. Let's show you what that looks like on your own catalog or workflow.