Five engineering principles that define how RCT Labs designs, builds, and delivers AI infrastructure.
We start from the desired future state — a validated, production-ready system — and work backward to the minimal component set required to reach it. This eliminates premature abstraction and keeps the architecture tightly scoped to what has been proven to work.
Mobile-first, zero-dollar infrastructure bootstrap, solo-developer execution — these are not limitations; they are quality probes. If a system cannot be understood and operated by one engineer with no budget, it is too complex. Constraints surface hidden dependencies, force prioritization, and produce leaner, more maintainable systems.
Every architectural decision maps to the FDIA equation: F = D^I × A. Data quality (D), integration depth (I), and Autonomy coefficient (A) drive component selection, API contracts, and rollout gates. A design that cannot be expressed in FDIA terms has not been sufficiently reasoned about.
No capability claim ships without a passing CI benchmark attached. The 4,849-test suite is not a coverage target — it is the deployment gate. 'Projected GAIA benchmark: 84–89%' carries the qualifier 'pending formal leaderboard validation' because evidence-quality standards apply to our own work as much as to our competitors.
JITNA RFC-001 is published. The FDIA specification is documented. Architecture decisions are logged. We default to openness not as a marketing position, but because verifiable systems require externally readable specifications. What cannot be inspected cannot be trusted.
Every principle here is backed by working code, documented specifications, and passing tests.