Mykhailo R.

Project overview:

Developed an intelligent automation system to handle Tier-1 customer inquiries for a microsite building platform. By leveraging advanced Retrieval-Augmented Generation (RAG), the system analyzes a knowledge base of over 280 articles to provide instant and accurate responses. Designed to solve scalability challenges by automating repetitive, low-complexity questions that previously overwhelmed the human support team.

Responsibilities:

• Designed a sophisticated state-based graph architecture to route queries, grade document relevance, and manage multi-step support workflows
• Implemented a hybrid semantic search layer featuring query expansion and cross-encoder reranking to ensure precise information retrieval
• Developed an automated 'watchdog' infrastructure for incremental indexing, allowing the system to self-update whenever documentation changes
• Integrated built-in validation mechanisms and source attribution to ensure all AI responses are grounded in official documentation
• Containerized the application for seamless cloud deployment and built an internal monitoring dashboard for real-time performance tracking

Achievements:

• Achieved a 60–80% reduction in routine ticket volume • Provided a positive ROI within 90 days of implementation • Enabled 24/7 support availability with sub-second response times • Maintained high-fidelity accuracy through automated hallucination detection

Technology stack:

Project overview:

Built a sophisticated personal productivity automation system based on a Multi-Agent Architecture to streamline tasks and professional collaboration. The system integrates with Google Workspace, Jira, Zoom, and Confluence to provide unified task management, automated scheduling, and intelligent email triage. A Supervisor Agent orchestrates specialized sub-agents that autonomously handle complex workflows like research and meeting coordination while maintaining human-in-the-loop security.

Responsibilities:

• Architected a multi-agent graph system using LangGraph, featuring a central Supervisor Agent that intelligently delegates requests to specialized agents for tasks, calendar management, and research
• Developed a bidirectional synchronization engine between Google Tasks and Jira to provide a unified priority view and eliminate manual task duplication
• Implemented an advanced 'LongMemory' system utilizing recursive summarization and context pruning to maintain high-fidelity context across extended sessions without exceeding token limits
• Designed a secure 'Human-in-the-Loop' validation layer for critical email communications, ensuring high-priority responses require explicit approval while routine drafts are handled autonomously
• Integrated comprehensive analytics and ROI tracking to monitor real-time message volume, per- agent token consumption, and 'estimated time saved' metrics

Achievements:

• Reduced operational costs and LLM token consumption by 40–60% through a custom multi-layer caching infrastructure • Reclaimed an estimated 10–15 hours per week for users by automating routine coordination and research tasks • Achieved sub-second response times for cached queries, significantly improving user experience and decision-making speed

Technology stack: