Practical guidance for taking AI products global, covering international user needs, privacy engineering, compliance, localization, subscription pricing, token-cost economics, SaaS business models, data security, growth channels, and scalable go-to-market architecture.
A practical EU AI Act technical compliance guide for high-risk AI systems. Covers risk management, data governance, logging, transparency, human oversight, accuracy, robustness, cybersecurity, documentation, and engineering implementation patterns.
A production privacy-engineering guide for AI products: map purpose and legal basis, trace prompts through embeddings and models, enforce controller and processor boundaries, propagate deletion and correction, produce DSAR evidence, and implement GDPR and CCPA rights without assuming derived data is anonymous.
A production guide to AI SaaS pricing: separate value and charge metrics, build an immutable usage ledger, settle retries and refunds correctly, measure cohort and tail unit economics, design hybrid packaging, and migrate price books without breaking customer trust.
A production guide to multilingual AI applications: define an application-owned locale contract with BCP 47 and CLDR, localize prompts by intent, filter RAG by language and jurisdiction, constrain tool behavior, evaluate native-language tasks, and release each market through explicit quality and safety gates.