AI product development company in Vietnam, from roadmap to an AI system you can operate

• Companies with an AI pilot stuck before production. The demo works, but data access, quality checks, permissions and post-launch ownership are still open.
• Product teams adding AI to an existing product, where AI has to work inside real user journeys, not in a separate chatbot tab.
• Regulated or data-sensitive teams that need private cloud, VPC or on-premise options.

Product type	Typical stack	Public example
AI inside an existing product journey	LLM, product UX, AWS	TPS AI: the T'FOX chatbot and AI Insight inside the TPS Mobile AI trading app
Governed AI analytics	Natural language over certified metrics	Presight, CoderPush's governed AI data analyst that ties answers to certified metrics
Assistants grounded in your content	LLM, retrieval, human escalation	Blue Sky Academy: a school chatbot that routes personal or sensitive questions to staff
On-device AI	Optimised models on mobile	Deepfake detection with Ensign InfoSecurity, showcased at Google I/O 2025
Agent workflows with review	Agents, tools, permissions, evals	Agentic Development
Data and cloud foundations for AI	Pipelines, vector search, governance, AWS	Cloud Transformation

1. Discovery. Clarify the business goal, user workflow, operating constraint and first useful release.
2. Workflow map. Make user decisions, data paths, approvals and failure modes explicit before writing code.
3. Data and security review. Define what data the AI can see, where it lives and who can access it.
4. Eval set. Build quality checks for answers, actions, edge cases and expected behaviour.
5. Guardrails and rollback. Put permissions, escalation, logs and fallback behaviour into the launch scope.
6. Launch. Ship with product UX, cloud architecture, observability and release controls.
7. Operate. Track adoption, reliability, latency, cost, value and model quality after release.

• AI and LLM: LLM applications, RAG systems, agent workflows, evals and monitoring
• Data: data pipelines, vector search, metric governance, permission models
• Cloud on AWS: secure foundations, GenAI services, observability, cost controls
• Product engineering: web and mobile UX, APIs, integrations, release management

• Product discovery sprint
• AI product development squad
• Embedded senior engineers (see Hire AI engineers in Vietnam)
• Scoped project or performance-based engagement
• Cloud and data foundation work

Ownership and security. NDA, IP ownership and a security review are part of the engagement. You own the code and the accounts. On AWS we build in your accounts under your IAM, and you can revoke our access at any time.

Where your data lives. We design around private-cloud, VPC or on-premise constraints, and confirm the specific requirements during discovery.

The full bank offer is on Production AI for Vietnamese banks.

Use cases we build for banks and securities firms:

• Fraud and risk AI: risk signals, suspicious-pattern detection, alert triage, analyst workflows
• Credit scoring and underwriting: decision support with explainable inputs and review paths
• Market and research automation: for securities and capital-markets teams, supported by Promvis, our Vietnam financial data infrastructure
• Customer-service copilots: support, onboarding and self-service journeys
• Compliance and AML copilots: human-reviewed document checks, escalation and audit trails
• Governed data platforms: clear metrics, permissions and lineage

Financial-services work: TPS AI for Tien Phong Securities. See more banking and fintech case studies.

We build controls for permission boundaries, audit logging, human review, fallback behaviour and security review. Any formal SBV, AML or institution-specific compliance statement should be validated with your compliance team.

We have shipped product and engineering work in EdTech, marketplaces, ecommerce, SaaS and corporate services, project management, Web3 and cybersecurity. See all case studies.