How can teams assess AI systems that can change their own path?
Evaluation frameworks, centralized evidence, assessment workflows and governance-oriented reporting for multi-agent AI applications.
Conceived the problem space, researched evaluation approaches, shaped the framework and proof of concept, and drove the implementation into reusable evaluation and governance capabilities.
Can different agent protocols feel like one developer experience?
Unified developer abstractions for creating, hosting, orchestrating and consuming tools and agents.
Conceived the need for a unified developer experience, researched the MCP/A2A boundary, shaped the abstraction and proof of concept, and drove its implementation into a reusable SDK pattern.
How do you make enterprise agent experimentation reusable?
Composable agent orchestration, workflow composition, tool integration and extensible application architecture.
Identified the gap, researched reusable agentic application patterns, shaped the architecture and proof of concept, and drove implementation of the composable framework.
How can synthetic data shorten the path from idea to experiment?
LLM-powered generation of domain-specific datasets for travel, finance and retail scenarios.
Conceived the opportunity, researched generation approaches, developed the proof of concept and implemented the system to generate up to 1 GB of domain-specific data in under 10 minutes.
How can AI help turn rough audit findings into review-ready writing?
Grammar correction, tone normalization, readability enhancement and structured finding generation.
Shaped the idea, researched the language-assistance problem, built the proof of concept and implemented the AI writing workflow, reducing audit review cycles from one week to half a day.