Sniffopotamus

Overview

Sniffopotamus is a fragrance intelligence platform built on one conviction: scent data should be trusted, not guessed. Public fragrance databases are full of duplicates, clones listed as originals and invented note pyramids — and AI chatbots make it worse by confidently filling gaps with fiction. How it works. At the core is a governed knowledge kernel. Every request — chat, app, or API — passes through the same pipeline: an intent router works out what's being asked, an entity resolver pins down exactly which fragrance is meant (your owned bottle, never a lookalike clone), and an action gate decides whether the system can act, must ask, or sends the change to human review. Nothing writes to the database without passing that gate, and every decision is traced. Three layers of knowledge. A structured fragrance graph holds the hard facts — brands, notes, accords, perfumers, reformulations, clone relationships, layering rules. A curated corpus holds the prose — perfume science, house styles, layering wisdom — every piece reviewed and source-backed before publishing. And private user memory holds what matters personally: your wears, your dislikes, your partner's favourites — kept strictly inside your own boundary. For collectors and for commerce. Consumers get a personal scent companion: collection tracking, wear logging, weather-aware "what should I wear tonight" advice and layering suggestions grounded in real rules. Commercially, the same kernel packages as a Fragrance Data API, fuzzy-match service, Shopify scent-finder widgets and a white-label brain for retailers and fragrance houses — the defensible asset being the verified data itself.

The problem it solves

Fragrance data is a mess. Public databases are riddled with duplicates, clones listed as originals, and made-up note pyramids — and AI chatbots happily invent the rest. Collectors have no trustworthy way to track what they own, what they've worn, and what to buy next, and fragrance retailers have no clean data engine to build on. Sniffopotamus solves this with a governed fragrance intelligence kernel: every fact passes through a verification gate before it's trusted, every answer cites its source, clones can never masquerade as originals, and uncertain data goes to human review instead of being guessed. The result is a personal fragrance brain that knows your cabinet, your wears, your partner's tastes and the actual science of perfume — and never makes things up.

Who it's for

Two audiences. Consumers: fragrance collectors and couples who want to track their collection, log wears, get wardrobe and layering advice, and discover their next bottle. Commercial: fragrance retailers, niche houses and Shopify stores who need a clean fragrance data API, fuzzy matching, scent-finder widgets and catalogue cleanup — a white-label fragrance brain without building one themselves.

Technology & approach

• API Service
• E-commerce
• Web App
• Non-Health SaaS
• Alpha
• Vercel
• TypeScript
• Node.js
• Next.js
• Supabase
• Subscription