AI for Technology Companies in Vietnam

Vietnam's AI Law, effective March 2026, introduces a risk-based classification system (high, medium, low risk). High-risk AI systems require mandatory pre-market conformity assessments and human oversight. We help you assess which classification applies to your use case and prepare the necessary governance documentation before enforcement begins.

Vietnam's Personal Data Protection Law (effective January 2026) requires explicit prior consent for data processing, 72-hour breach notification to the Ministry of Public Security, and impact assessments for cross-border data transfers. The Cybersecurity Law also requires 24-month local data storage for Vietnamese user data. Our implementation includes compliance checkpoints for all these requirements.

Yes. Given that only 15-20% of Vietnam's workforce has business-level English, we deliver all training with Vietnamese-language materials and bilingual facilitators. Hands-on exercises use Vietnamese-language examples and locally relevant case studies.

Vietnam offers several incentive mechanisms: preferential CIT rates of 10% for up to 15 years for high-tech projects (vs. standard 20%), the Investment Support Fund (Decree 182/2024) with cash grants for qualifying AI investments, and the NIC Digital Talent Development Program for workforce training. We help you document your investment to qualify for applicable incentives.

While FPT (USD 2.47B revenue) and VinAI offer strong local capability, Pertama Partners brings cross-ASEAN implementation experience with deep regulatory expertise across multiple jurisdictions. We provide structured methodology validated across Southeast Asian markets, combined with Vietnam-specific compliance guidance that reflects the latest AI Law, PDPL, and Cybersecurity Law requirements.

Data shows AI coding assistants increase both velocity AND quality when used correctly. Developers write code 30-50% faster but must still understand what they're building (AI is autocomplete++, not autopilot). Module 1 teaches best practices: use AI for boilerplate, tests, and documentation while developers focus on architecture, business logic, and code review. Companies using AI coding tools report higher developer satisfaction and lower bug rates.

The training is tech-stack agnostic in principles but customized in implementation. During Content Customisation, we tailor all labs to your stack (language, frameworks, cloud provider, CI/CD tools). For example: React/Node teams build Next.js AI features, Python teams build FastAPI microservices, Go teams optimize Kubernetes deployments. You'll leave with AI workflows that integrate directly into your existing development environment.

Comprehensive programme (6 modules, 4-5 days) covers AI across engineering, product, and customer operations — ideal for companies wanting organization-wide AI transformation. Specialist solutions (4-5 modules, 3-4 days) dive deeper into one domain (DevOps automation, product intelligence, support optimization) — ideal for teams with specific pain points. Choose comprehensive for broad AI literacy, specialist for deep expertise in one area.

Module 6 covers comprehensive ROI frameworks. Engineering AI: measure velocity (story points per sprint, lead time), quality (bug rates, test coverage). Product AI: measure retention cohorts, feature adoption, conversion rates. Customer AI: measure churn rate, NPS, support ticket volume. Infrastructure AI: measure MTTR, incident frequency, uptime SLAs. We'll help you establish baseline metrics before AI deployment and track improvements quarter-over-quarter.

Both. Early-stage startups (Series A and earlier) focus on AI quick wins: coding assistants for faster MVP building, support chatbots to delay hiring support team, basic product analytics. Growth-stage companies (Series B+) deploy predictive churn models, advanced DevOps automation, and AI-powered product features. We customize content based on your stage, team size, and immediate priorities during Discovery phase.