AI Agents
Reasoning, planning, memory, agent boundaries, and controlled execution patterns.
Research Lab
Project KAI research focuses on what can be measured, simulated, reviewed, and improved: what to automate, what to keep manual, how domain systems learn, and how consequential work can remain governed as capability expands.
Reasoning, planning, memory, agent boundaries, and controlled execution patterns.
Repeatable workflows, manual approval gates, launch operations, and practical system design.
Market observation, strategy research, backtesting, simulation, risk governance, and Alpaca Paper validation—never presented here as live-money execution.
Reports, validation records, roadmaps, decision logs, and structured knowledge reuse.
Human oversight, rollback plans, protected directories, frozen baselines, and no-automation boundaries.
Structured reports, bounded evaluations, decision records, and honest labels wherever data access or automation remains incomplete.