Developing agentic systems that augment human capabilities through human-in-the-loop AI.
Agent evaluates investment opportunities using financial analysis and market data synthesis.
Agent assists in preparing tax returns by guiding collection and validating information.
Voice-first agent for household grocery management and inventory tracking.
Helps in preparation before a negotiation and to make decisions as negotiations unfold.
Agent to aid voters in their selection among presidential candidates.
Brainstorming and validating AI agent concepts that solve real operational pain points through domain research and expert interviews.
Building functional proof-of-concepts to validate technical approach and demonstrate core capabilities with human-in-the-loop validation.
Collaborating with domain experts and early adopters to refine the solution through real-world testing and co-development.
Building a production-ready MVP with enhanced features, robust error handling, and scalable deployment architecture.
Custom agent orchestration systems for complex workflow automation with human oversight controls.
Systems that synthesize domain-specific knowledge with real-time data and expert validation.
Interface patterns that enhance rather than replace human expertise through intelligent assistance.
Agentic systems transform minimal human effort into massive impact. This is the power of AI in knowledge work when precisely integrated with human expertise.
Systematic analysis of existing workflows identifies areas for AI leverage, and process redesign defines where to implement agentic workflows for reliable, high-volume tasks. Human expertise is focused mostly on complex, high-value decisions and final quality checks.
An intelligent mixture of augmentation and control creates a superior, more productive, and reliable process than either humans or agents could achieve alone.
A digital workshop for exploring how AI Agents can reshape knowledge work.
Rather than specializing in one industry, this sandbox applies a systems-thinking approach to various fields—studying the domain constraints, identifying the friction, and prototyping the agentic solution that removes it.
I started this journey into AI learning, experimentation, and prototyping in February 2025, alongside the beginning of my studies in AI/ML at the Math Department in ETH Zürich.
