Every day, thousands of events surface across hundreds of fragmented, multilingual, half-duplicated sources. Wombie runs them through a machine-learning pipeline that understands what each one is, merges the duplicates, and turns the noise into a single, structured map of where to be tonight.
A concert, a trivia night, a film screening, a street festival — every source publishes them differently, in different languages, with different fields, half of them duplicated three times over. The real world does not arrive pre-structured.
Rules and scrapers alone fall apart at this scale. Reading messy human text and deciding what it means is exactly what modern AI is for — so machine-learning models sit at the center of everything Wombie does, from the first byte ingested to the recommendation that reaches you.
Every event flows through three stages. The middle one — understanding — is pure machine learning, and it is where the magic happens.
Hundreds of sources, scraped continuously. Inconsistent formats, missing fields, listings in a dozen languages, duplicates everywhere.
Transformer-based models classify each event, resolve duplicates into one, enrich it with structure, and score its quality — no hand-written rules required.
A single, de-duplicated, categorized catalog of real-world events — searchable, rankable, and ready to put people in the same room.
Machine learning is not a feature bolted onto Wombie. It is the substrate the whole product runs on.
Our models understand an event from a single sentence — concert, theatre, trivia, screening — with no labelled training data and across languages. New categories cost a string, not a dataset.
The same event shows up on ten sites under ten names. Language-aware fuzzy matching and semantic similarity collapse them into one canonical record — so you see the night, not the noise.
Models infer category, link screenings to a film catalog, normalize venues and places, and score completeness — turning a bare listing into a rich, trustworthy entry.
We are extending the same natural-language models inward: reading the tone of group chats to catch last-minute decline cascades and nudge gently, so plans survive contact with real life.
Vector embeddings let people search by intent — "something low-key on a Tuesday" — and power recommendations built on co-attendance, not engagement-bait ranking.
A small team ships at the pace of a large one because the system that builds the system is itself AI. New sources, new models, new surfaces — generated, reviewed, shipped.
Wombie serves transformer models natively from its Elixir backend — compiled, fast, and built to scale onto cloud GPUs as we grow.
Pre-trained transformers loaded straight from the Hugging Face Hub and served in-app.
Tensor compute compiled through XLA for fast CPU inference today, GPU inference as we scale.
A serving path built to lift onto managed GPU infrastructure as event volume and model count grow.
Semantic representations of every event powering search-by-intent and similarity matching.
Every direction below leans harder on AI — and on the cloud GPU capacity to run it.
Find events by vibe and intent, not keywords — at scale, across languages.
Models that suggest who and what to do next from the real graph of who showed up together.
Large language models filling gaps, summarizing, and translating listings into one clean shape.
Bigger, multilingual models served from cloud GPUs as event volume and model count climb.
AI is how we read a chaotic world and hand you somewhere to be. We are just getting started — and we are looking for partners, infrastructure, and people who want to build it.