Claw Learn
Voice-powered AI tutor
Data Scientist & AI Engineer, and an MSc Data Science student at Óbuda University in Budapest. I build machine learning systems, data pipelines, and agentic AI tools, with experience across banking, analytics, and e-commerce.
My work spans fraud detection, NLP assistants, large-scale analytics, and AI-powered production workflows. I also perform research on different AI & Data Science Topics.
Career
AI Production Engineer · ContractSpice Media Production, Budapest : Agentic AI for e-commerce content, production, and post-production workflows.
Data ScientistKapital Bank, Baku : Fraud detection, cash-replenishment pipelines for 1,000+ ATMs, and an NLP customer-service assistant.
Data ScientistBIRainy, Baku : Python and SQL analytics, Power BI reporting, and OCR document pipelines.
Education
MSc in Data ScienceÓbuda University, Budapest : Machine learning, statistical analysis, data pipelines, and reproducible research. TDK paper on benchmarking open-source LLMs for Azerbaijani, Fall 2025.
BSc in Information TechnologyAzerbaijan State Oil and Industry University : Google DSC, Innovation Club, and Debate Club; workshops, hackathons, and interdisciplinary teams.
Based in Budapest, Hungary.
The challenge
Make abstract mathematics easier to understand through conversation and visual explanations. Claw Learn lets a learner ask a math or physics question and explore the answer through live animation.
ElevenHacks #10 · 2nd place
2026
Arzuman Abbasov
What we built
A useful tutor needs more than a spoken answer. Voice interaction, a teaching plan, and visual scenes need to stay synchronized while leaving room for interruptions and follow-up questions.
My contribution
I built a voice-first learning experience that connects the ElevenLabs Speech Engine over WebRTC with an AI scene planner and a custom Canvas renderer. It generates animated explanations for each question and supports voice or text input.
Outcome & demo
Second place at ElevenHacks #10. The project is open source under the MIT license, with a live demo and a three-minute walkthrough. The winners announcement is shown here; the repository documents the architecture and setup.
