Building AI systems responsibly means more than following regulations. It means designing honesty, safety, and accountability into the architecture itself.
Every public claim is qualified with evidence. We write 'Benchmark hallucination target: 0.3%' and 'FDIA benchmark accuracy: 0.92 vs ~0.65 baseline' — never stripped of context. We do not inflate numbers or delete qualifiers under time pressure.
Thailand's PDPA Section 33 explainability requirements are built into the system design from day one. Right-to-erasure flows, cross-border transfer documentation, and data-minimization principles are first-class design constraints for every RCT platform.
The FDIA equation (F = D^I × A) includes an Autonomy coefficient (A). When A = 0, the system cannot act unilaterally — a hard architectural kill switch. Anti-prompt-injection layers prevent instruction-override attacks. Constitutional constraints are structural invariants, not configurable toggles.
RCT Labs does not ship features without validation gates. Backend-validated coverage currently stands at 66.7%, with a public target of 100%. Each stage is documented and rollout metrics are visible. We prefer slower, validated delivery over fast, unverified shipping.
We build AI systems that augment human judgment, not replace it. We do not claim AGI timelines, use phrases like 'AI will replace all jobs', or issue press releases around inflated capability claims. Our marketing language uses the same qualifier standards as our engineering documentation.
Our claims, benchmarks, and validation results are publicly documented. Review the evidence behind every number.