Kybercel
A local-first governance system that places deterministic, versioned constraints at explicit AI action boundaries.
Problem
AI agents can move generated work into operational systems, while prompts alone do not provide deterministic enforcement of business, architecture, security, or approval rules.
My contribution
I took Kybercel from product thesis through architecture, product design, implementation, automated validation, and deployment. I directed specialized AI-agent workflows across product management, UX, architecture, development, QA, technical writing, and review while leading product strategy, market positioning, cost planning, and investor/pitch preparation.
Technology
The product family uses a TypeScript monorepo, deterministic core packages, a CLI, an MCP server, a Next.js workbench, PostgreSQL, runtime adapters, audit controls, and automated validation.
Decisions
I designed a deterministic TypeScript core shared by the CLI, MCP server, Workbench, and runtime adapters so rule semantics, action-boundary decisions, and evidence remain consistent across delivery surfaces.
Implemented capability
I built governed workflows for repository and AI I/O review, approval gates, explicit action boundaries, redaction-aware audit evidence, remediation, source-backed policy packs, and fail-closed or degraded behavior that reports measured control strength without overstating prevention.
Future direction
Planned direction includes expanding source-backed domain policy packs and governed delivery surfaces; this record does not make a public roadmap or delivery-date commitment.
Hiring relevance
The work demonstrates current judgment across platform architecture, developer workflows, security boundaries, governance, product delivery, and honest reporting of control strength.