Startup founder & AI builder. Building AI at CTOgram and creating ProfSidekick for professors. Previously co-founded Clout, SWE @ Qlub, RA @ NYUAD CHI Lab. Passionate about startups, AI, and products that scale.

Hi, I'm Assylbek, a software engineer and startup founder passionate about building innovative AI products. I am the solo developer of ProfSidekick, an AI-powered teaching assistant for professors, and I’m also working on the AI solution at CTOgram, a platform for car services and spare parts.
Previously, I co-founded Clout to help influencers create media kits and collaborate with brands, and worked at Qlub on POS integrations and analytics. My journey started at the NYUAD CHI Lab, where I built real-time analytics tools.
I thrive in fast-moving environments, love designing scalable solutions, and I’m always open to new collaborations.

GPA: 3.77 / 4.00
2021 - 2025
Completed a comprehensive computer science curriculum with a focus on software engineering, distributed systems, and artificial intelligence.
• Built ProfSidekick, a full-stack AI teaching assistant platform that transforms static presentations intovoice-interactive lessons with real-time AI tutoring and slide control..
• Engineered scalable backend (FastAPI, PostgreSQL, Redis) with 25+ REST APIs, 6+ database models, deployed via Docker/Fly.io.
• Integrated OpenAI Realtime, Vision, and Function Calling APIs, enabling live voice Q&A, automated slide explanations, and AI-driven slide navigation.
• Implemented real-time communication via WebRTC and custom event streaming, enabling live voice processing, AI guidance, and session syncing across users.
• Engineered an LLM-powered assistant, CTOgram, for mobile and Telegram, streamlining car service and part requests; achieved a user satisfaction score of 4.8 out of 5 based on post-interaction surveys.
• Developed FastMCP, a custom Remote Model Context Protocol (MCP) server integrated with the Responses API, enabling real-time tool execution and structured multi-turn conversations.
• Improved UX with dynamic UI elements (buttons, keyboards) based on MCP outputs, e.g., showing user’s cars via phone lookup for instant selection.
• Integrated PostgreSQL with Redis caching layer, resulting in a 30% reduction in database query times; streamlined data access for 4 core services utilized by 5+ engineers across the organization.
• Led integration for Pizza Express and 20+ restaurants across 4 regions, enabling 50K+ monthly transactions via Python microservices, REST APIs, MSSQL, and Redis.
• Delivered integrations for 5+ global POS providers, shortening onboarding timelines by 40%, by standardizing API development, architecture design, and effective stakeholder collaboration.
• Implemented LLM-based categorization system, achieving 94%+ accuracy in automated item classification, improving restaurant analytics and operational insights.
• Improved system reliability and decreased troubleshooting time by 40% by implementing distributed tracing (Jaeger) and real-time monitoring dashboards (Grafana).
• Developed and deployed QR-based menu ordering system for a key POS provider, increasing ordering efficiency by 25% and enhancing customer experience through seamless integration.
• Developed a Chrome extension capturing 2M+ user events for productivity insights in real-time.
• Built secure Flask backend with 99.9% reliability using JWT authentication for real-time event collection.
• Optimized PostgreSQL, decreasing query times by 40% with indexing and partitioning.
• Deployed a scalable Flask application on AWS using EC2, Elastic Load Balancing, Auto Scaling.

Clout empowers influencers by automatically generating dynamic, professional media kits with personalized URLs, updating daily with real-time analytics and insights from connected social media accounts. This simplifies influencer-brand interactions and dramatically reduces the time and effort required compared to traditional methods.

Developed a fast and responsive API using Large Language Models (LLMs) to classify customer-reported car issues into predefined categories within 0.4-1 second. This standalone feature was built for CTOgram, enhancing their ability to quickly match 178,000 customers with relevant car services from over 1,100 providers.

Developed an API-driven reporting system integrated with Google Sheets to gather data from various sources and classify conversations between AI agents and customers. Used LLM-based classification to evaluate response performance, accuracy, and time efficiency, providing insights into the effectiveness of AI agents across business applications.
You can also request my resume via email at assylbek.s@nyu.edu
I'm currently available for freelance work, consulting opportunities, and startup collaborations. Feel free to reach out if you're interested in working together!
I typically respond to emails within 1 hour.
Telegram/Email are the best way to reach out to me. Feel free to connect on LinkedIn.