KOOYA / AI DIGEST / MANILA
Testing what works, managing risk and keeping people in control.
Browse the library ↓Showing 1–11 of 11 articles
How Philippine marketing teams can review AI-generated likenesses: NPC guidance, reference permissions, vendor uploads, campaign approvals and removal.
A practical guide to AI invoice intake for Philippine teams: extract fields, check duplicates, resolve exceptions and preserve payment approval.
A Philippine operating note on AI call-quality scoring: NPC guidance, employee notice, evidence-linked coaching, language checks and score disputes.
An evidence-based AI vendor selection guide for Philippine teams: compare scope, data handling, operating costs, acceptance, support and exit terms.
A practical guide to company-document AI search in the Philippines: retrieval, permissions, source freshness, citations, privacy review and pilot acceptance tests.
Kooya explains Pino SI’s planned coding comparison, the language and compute confounds to examine, and how executable tests can support a careful conclusion.
Kooya’s research note separates mathematical answers, scientific reasoning and machine-checked proofs in Pino SI’s planned STEM work.
A research note on Pino SI’s planned defensive cybersecurity focus: understandable scam and alert explanations, bounded evaluation and human review.
Explore Pino SI’s planned agriculture, supply-chain, preparedness and infrastructure POCs, with distinct tests and current, cited information.
A source-based look at DOST health AI: AYUHON’s pediatric research, PASYENTE user testing and the ASEAN regional plan still awaiting endorsement.
A practical AI pilot evaluation checklist for Philippine teams: test real tasks, Filipino and English inputs, permissions, handoffs, cost, and release readiness.
