Vietnam has long been associated with software outsourcing, but its technology industry is increasingly involved in more specialized development work, including generative AI, machine learning, computer vision, and AI agents.
For companies planning to build AI-powered applications, Vietnam offers an interesting range of potential development partners.
However, choosing an AI development company requires more than comparing hourly rates or checking whether a vendor has experience with ChatGPT APIs.
The real question is whether a development team can turn an AI prototype into a secure, maintainable application that works within an existing business environment.
Let's look at the technical considerations that matter.
1. AI development is more than model integration
One of the easiest ways to start building a generative AI application is to connect an existing large language model to a user interface.
A simple architecture might look like this:
User
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v
Web Application
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v
LLM API
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Generated Response
This works well for experimentation.
But enterprise applications usually need more components.
Consider an internal knowledge assistant that answers questions using company documents.
A production-oriented architecture may look more like this:
Enterprise Data
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Data Ingestion
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v
Processing & Indexing
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Search / Retrieval
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User --> Authentication --> RAG Pipeline
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| v
| LLM Service
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v v
Access Control --> Response
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v
Logs & Evaluation
The model is only one part of the system.
A development partner also needs experience with data pipelines, authentication, APIs, infrastructure, and application architecture.
This distinction is especially important when evaluating companies that advertise generative AI development services.
2. Evaluate the team's experience with production RAG
Retrieval-Augmented Generation (RAG) is a common approach for building AI applications that use private or domain-specific information.
It can help an LLM generate answers grounded in retrieved documents instead of relying exclusively on its pretrained knowledge.
However, implementing RAG involves several engineering decisions.
Document processing
Enterprise data rarely arrives in a consistent format.
Documents may include PDFs, spreadsheets, presentations, scanned files, and records from internal systems.
A development team needs to consider:
- Text extraction and document parsing
- Chunking strategies
- Metadata management
- Data synchronization
- Document updates and deletion
Retrieval quality
Vector search alone is not always sufficient.
Depending on the application, hybrid search, metadata filtering, or reranking may improve retrieval quality.
The best approach depends on the structure of the data and the questions users are expected to ask.
Access control
A critical requirement is ensuring that users can retrieve only information they are authorized to access.
Permission checks must be enforced by the application architecture, not left to the language model's judgment.
Evaluation
A RAG system should be tested using representative questions and documents.
Useful evaluation dimensions include retrieval relevance, answer correctness, citation quality, latency, and cost.
When selecting an AI development partner, ask how the team evaluates these aspects rather than focusing only on model selection.
3. AI agents introduce additional engineering challenges
AI agents are becoming an increasingly important area of enterprise AI development.
Unlike a basic chatbot, an AI agent may interact with external tools, retrieve information, execute workflows, and perform multiple steps to complete a task.
For example:
User Request
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AI Agent
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+--> Retrieve Information
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+--> Call Internal API
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+--> Process Results
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+--> Request Human Approval
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Final Output
This creates new requirements.
An agent may need permission to interact with business systems, but those permissions must be carefully controlled.
Developers should consider:
- Tool access and authorization
- Input and output validation
- Error handling and retries
- Human approval for sensitive actions
- Audit logs
- Monitoring and cost controls
It is also important to distinguish between workflows that genuinely benefit from agentic behavior and tasks that can be handled more reliably through conventional automation.
A capable AI development partner should be able to explain when an AI agent is appropriate—and when it is not.
4. Look for experience beyond AI models
Many enterprise AI projects involve existing applications and infrastructure.
An organization might want to integrate AI into a CRM, ERP, customer support platform, or internal knowledge system.
This requires more than machine learning expertise.
A development team may need capabilities in:
| Area | Why it matters |
|---|---|
| Backend development | Connect AI services with business logic |
| API integration | Exchange data with existing systems |
| Cloud infrastructure | Deploy and scale AI applications |
| Data engineering | Prepare and manage enterprise data |
| Security | Protect sensitive information and system access |
| DevOps / MLOps | Monitor, deploy, and maintain applications |
This is one reason why established software engineering companies can be relevant partners for enterprise AI projects.
Their experience with application development and system integration can complement their AI capabilities.
5. Vietnam's AI development ecosystem
Vietnam has several technology companies working across AI, software engineering, and digital transformation.
FPT Software is known for global IT services and enterprise digital transformation. CMC Corporation works across AI, cloud, data, and cybersecurity. Viettel combines telecommunications and digital infrastructure with technology development, while VinAI is associated with AI research and computer vision.
Another example is SotaTek Japan, a Vietnam-based technology company operating in Japan through SotaTek Japan.
Its work covers generative AI, AI agents, software development, cloud technologies, and enterprise system integration.
SotaTek Japan also develops its own AI products, including:
- SotaAgents: An enterprise AI agent platform
- NoteX: An AI-powered meeting and knowledge management platform
- Saydi: A real-time AI speech translation service
For engineering teams evaluating development partners, this combination of AI application development and broader software engineering capabilities is worth examining.
It illustrates why vendor selection should consider the complete implementation process, not just the ability to build a model demonstration.
For a broader overview of companies operating in Vietnam's AI ecosystem, SotaTek Japan has published a Japanese-language guide:
AI Development Companies in Vietnam: Company Overview and Selection Criteria
6. Questions to ask before starting a project
Before choosing an AI development partner, engineering teams should prepare a few practical questions.
Architecture
Can the team explain the proposed system architecture and its trade-offs?
Security
How will sensitive data, user permissions, and external AI services be managed?
Evaluation
What metrics will determine whether the AI system performs adequately?
Integration
Can the solution connect with existing enterprise applications?
Production readiness
How will the application handle monitoring, failures, updates, and ongoing costs?
Ownership
Who will own the source code, infrastructure configuration, and project documentation?
The answers will reveal much more than a general statement about AI expertise.
Final thoughts
Vietnam is becoming an increasingly relevant location for AI application development, supported by its software engineering ecosystem and growing interest in advanced AI technologies.
But choosing the right partner is fundamentally an engineering decision.
A successful enterprise AI application requires reliable architecture, data integration, security, evaluation, and long-term maintenance.
Whether the development team is based in Vietnam or elsewhere, these requirements remain the same.
The best AI development partner is not necessarily the one that can produce the most impressive demo. It is the one that can help turn a useful idea into a reliable production system.
Suggested tags: #ai #generativeai #softwaredevelopment #architecture
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