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A conversational product recommendation system which is like a shopping assistant. Understands user’s intent, keeps track of session state, and asks follow-up questions before recommending products.
A detector that survives real-world image damage and generalises across AI generators. One extra training source took cross-generator accuracy from 49% to 97%, at a cost of 0.5 points.
An autonomous ML research agent for recommender systems. It forms hypotheses, patches its own code, executes iterative optimization loops, and verifies results via bootstrap on sealed splits.
From conversation to the right product—precise and fast.
Hazelnut is a conversational shopping agent, given a short customer message, the agent returns ranked catalog recommendations, surfacing the target product as early and as highly ranked as possible.
An LLM agent that runs the full ML research loop on a frozen benchmark: proposes an experiment, edits real model code, trains, and keeps it if a bootstrap test says the gain beats measurement noise.
Manage events with automated form generation, participant registration in one click, and event schedules at a glance.
“Filtering the noise so only real, relevant reviews shine through. Our ML system flags spam and rants, enforcing policies so Google reviews stay clean, smart, and reliable.”
Online reviews are plagued by spam and irrelevance. Our BERT pipeline flags poor reviews, which increases user trust, business fairness, and platform credibility. It's needed now more than ever.
Using a custom BERT model with a weighted loss function, our solution filters out irrelevant Google reviews, helping users quickly find the most relevant and trustworthy feedback
Watch. Earn. Boost. Watch ads and answer surveys to earn Sparks, then boost your favourite creators, with no cash needed.
