Govern the inputs
Define provenance, permitted use, quality controls, exclusions, retention, and enterprise data boundaries.
RAYA treats data governance, model evaluation, access control, release management, and human responsibility as parts of one lifecycle.
Define provenance, permitted use, quality controls, exclusions, retention, and enterprise data boundaries.
Test language quality, instruction following, safety behavior, factuality, and domain-specific risks.
Constrain tools, knowledge access, permissions, outputs, logging, and escalation paths.
Version models, document changes, monitor production behavior, and keep humans responsible for consequential decisions.
Source review, permitted use, quality, minimization, and retention
→Local-language tests, task quality, safety behavior, and risk scenarios
→Identity, permissions, retrieval boundaries, tools, and output constraints
→Versioning, monitoring, incident response, and accountable ownership
→No. Models can produce incorrect or inappropriate outputs. RAYA’s approach is to evaluate known workloads, apply controls, and design appropriate human review.
Local language patterns, cultural context, domain risks, and representative Indonesian tasks should be part of evaluation and post-training design.
Human organizations remain responsible. AI systems should support—not obscure—accountability for consequential decisions.