For a Vietnam-based company seeking a partner to take generative AI beyond a demo, the strongest shortlist is FPT Software/FPT Digital, CMC Global, Kyanon Digital, TMA Solutions and Rikkeisoft. They are not an objectively ranked set of market leaders: public evidence is largely company-authored, and each is better suited to a different kind of project. FPT is the broad enterprise-transformation choice; CMC fits data- and cloud-heavy programs; Kyanon is especially explicit about custom RAG applications; TMA is engineering-led; and Rikkeisoft offers a broad software-and-AI option.
How to read this shortlist
“Top” here means a researched shortlist, not a ranking by revenue, market share or independently audited delivery results. The comparison weighs explicit generative-AI capability, consulting depth, production engineering, security and governance evidence, Vietnam delivery capability, industry relevance and transparency. Company descriptions and product claims below are attributed to the providers; they are not independent validation of performance.
Consulting means helping define use cases, readiness, business case, governance and architecture. Implementation means building and integrating applications such as retrieval-augmented generation (RAG) assistants, copilots or agents. AI development outsourcing may supply engineering capacity without owning the strategy. A platform provides infrastructure or model services, which may be purchased separately from consulting.
| Company | Best fit | Vietnam connection | Publicly described GenAI scope | Evidence and pricing visibility |
|---|---|---|---|---|
| FPT Software / FPT Digital | Large-enterprise transformation and end-to-end delivery | Vietnam-based group with global services | AI readiness, enterprise consulting, assistants, implementation and platform ecosystem | Multiple service pages and platform pricing documentation; consulting price not stated |
| CMC Global | Data-, cloud- and integration-heavy enterprise AI | Part of Vietnam’s CMC technology group | Full-cycle AI and data services; ask for specific LLM and RAG examples | Broad capability descriptions; custom quote, price not stated |
| Kyanon Digital | Custom RAG, knowledge assistants and workflow copilots | Vietnam-based provider | Discovery through ingestion, architecture, deployment, integration and monitoring | Detailed public GenAI service description; price not stated |
| TMA Solutions | Engineering-heavy products and complex AI integration | Vietnam-based engineering provider | AI, agents, edge AI and generative-AI/LLM development claims | Mostly provider-authored capability material; price not stated |
| Rikkeisoft | Software projects that incorporate GenAI or voice AI | Vietnam-origin software provider | Generative-AI and speech/voice-AI offerings | Public service listing; detailed delivery evidence and price not stated |
1. FPT Software / FPT Digital: best for large-enterprise transformation
What it is suited to
FPT is the strongest fit on this list when an organization wants strategy and implementation alongside cloud, data, engineering and potential managed services. FPT Digital advertises AI-readiness assessment, enterprise AI consulting, tailored implementation and generative-AI-powered enterprise assistants for internal search, customer experience and workflow optimization (FPT Digital). FPT’s broader ecosystem includes FPT Software and FPT Smart Cloud.
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What to verify
FPT has promoted products including AI Mentor and CodeVista, but product claims and performance figures are FPT-reported, not independent proof of results for your use case (FPT’s AI4VN 2024 announcement). Separate the scope and charges for FPT Digital consulting, FPT Software engineering and any FPT Cloud platform in the proposal. FPT publishes AI Factory pricing documentation, but platform pricing is not the price of a complete custom implementation (AI Factory pricing documentation).
Main trade-off: The breadth may be excessive for a small startup or contained proof of concept, and a large-provider procurement and delivery model may be more structured than a small project needs. Treat this as the strongest large-enterprise option, not a universal best choice.
2. CMC Global: best for data- and cloud-heavy programs
What it is suited to
CMC Global describes full-cycle AI services spanning AI and data science, cloud-related implementation and enterprise use cases. Its public material discusses real-time data platforms, AI-powered support and data automation (CMC Global AI solutions). A CMC data-solutions overview also presents the group across AI, analytics and cloud, with exposure to banking, telecommunications and government; that is a provider-authored overview rather than independent customer validation (CMC data-solutions overview).
What to verify
The public case for CMC is clearer on broad AI, data and cloud capability than on detailed generative-AI implementation. Ask for production examples covering LLMs, RAG, agents, evaluation and post-launch monitoring. Confirm which CMC entity will contract, set architecture and deliver the work. Public project pricing is not stated; expect a custom proposal.
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Main trade-off: It is a credible fit where data foundations and system integration are central, but buyers should establish the team’s specific GenAI experience rather than infer it from general AI capabilities.
3. Kyanon Digital: best for custom RAG and knowledge assistants
What it is suited to
Kyanon explicitly markets generative-AI consulting and implementation in Vietnam. Its described process includes discovery, architecture design, secure data ingestion, deployment, integration, training, monitoring and improvement. It also describes RAG pipelines that split enterprise documents into knowledge chunks, store them in vector databases, retrieve relevant content and generate answers grounded in internal sources. Pinecone, Qdrant and Weaviate are examples it names, not components guaranteed in every project (Kyanon GenAI consulting).
The company also presents AI, GenAI and machine learning as part of consulting and data-transformation work, including workflow integration and custom model deployment (Kyanon consulting; Kyanon Analyse and Augment).
What to verify
Kyanon’s page gives differing indicative MVP timelines: roughly two to four months in one section and as little as four to six weeks in an FAQ. Treat these as marketing estimates, not delivery commitments; document access, integrations, security review and scope will affect schedule. Public case-study detail is limited, and featured outcomes are company-reported. Treat “no vendor lock-in” as a design goal to verify in architecture and contract terms. Public rates are not stated.
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4. TMA Solutions: best for engineering-heavy AI products
What it is suited to
TMA is an established Vietnam-based engineering provider whose published capabilities span AI, machine learning, data, computer vision, healthcare AI, AI agents and edge AI. Its 2026 company overview also presents an AI Agent Factory with no-code and low-code options (TMA AI development overview). TMA’s AI-outsourcing material includes generative AI and LLM work among its stated technical capabilities (TMA AI outsourcing overview).
What to verify
TMA’s strongest public positioning is engineering, outsourcing and applied AI. Do not assume that this includes board-level strategy, use-case prioritization or business-case ownership: ask whether TMA will lead discovery or provide an engineering team against a client-defined plan. Much of the public evidence, including rankings and workforce claims, is self-published. Public pricing is not stated.
Main trade-off: A strong candidate for product development and complex integration, particularly in areas such as telecom, healthcare, fintech or embedded systems, but confirm the consulting component if strategic guidance is essential.
5. Rikkeisoft: best for broad software delivery with AI components
What it is suited to
Rikkeisoft is a practical option for companies seeking a broad Vietnam-based software-development partner that can incorporate generative AI or voice interfaces into a larger application. Its official site lists generative-AI solutions and speech-processing/voice-AI capabilities alongside software development and IT outsourcing (Rikkeisoft).
What to verify
The public material available here is less detailed than Kyanon’s on RAG architecture, governance, model evaluation and production monitoring. Ask for named or anonymized production references and establish whether the proposal includes strategy, solution architecture, security testing and post-launch operations—or primarily custom development. Public pricing is not stated.
Main trade-off: A broad software-plus-AI option, especially when voice or application engineering is part of the brief, but require more evidence before relying on it for a governance-intensive GenAI program.
Rank #4
A global-delivery alternative: NashTech
NashTech is a credible alternative if “in Vietnam” means a provider with substantial Vietnam delivery capability rather than a Vietnam-headquartered company. Its careers material describes Vietnam-based data-science work in Da Nang and Ho Chi Minh City involving research, evaluation, fine-tuning and deployment of generative-AI and machine-learning models (NashTech data scientist role). NashTech also describes AI, generative AI and machine learning as part of its broader services (NashTech service overview).
Consider it instead of Rikkeisoft when international procurement, a larger global delivery organization or cross-country coordination matters more than a Vietnam-origin provider. The cited evidence establishes Vietnam-based expertise and service positioning, not a specific client outcome or project price.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a serious GenAI consulting engagement should cover
A provider should be able to explain how it will move from a business problem to a system that can be operated safely, not merely show a chatbot interface. For any shortlisted firm, establish who owns each stage:
- Use-case selection: identify a workflow where generative AI can improve a measurable outcome and define what must remain with a human.
- Readiness and risk: inspect source data, permissions, quality, regulatory constraints and likely failure impact.
- Model and architecture: choose a model and deployment pattern based on quality, latency, privacy, availability and cost requirements.
- Application and integration: connect the model to the relevant systems, identity controls and business workflows.
- Grounding: use RAG or another appropriate method to bring approved, current enterprise information into answers.
- Evaluation: test answer quality, retrieval, safety, latency and cost against representative tasks before release.
- Deployment: specify public cloud, private cloud, dedicated endpoint, on-premises or controlled SaaS requirements in the statement of work.
- Operations: monitor model behavior, access, usage and cost; refresh content, review incidents and improve the system after launch.
- Adoption: train users and define escalation and feedback processes so the tool fits actual work.
Choose the engagement to fit the job
| Buyer need | Shortlist starting point | Why |
|---|---|---|
| Large-enterprise transformation across strategy, implementation and platforms | FPT Software / FPT Digital | Broad consulting, engineering and cloud ecosystem |
| AI tied to data modernization and cloud integration | CMC Global | Its public positioning combines AI, data and cloud services |
| Internal knowledge assistant, document search or workflow copilot | Kyanon Digital | Explicit public description of RAG and end-to-end GenAI implementation |
| Complex product build or engineering capacity | TMA Solutions | Engineering-led AI and software capabilities |
| Software build with generative or voice-AI components | Rikkeisoft | Broad application development plus listed GenAI and speech offerings |
| International procurement and global delivery model | NashTech | Global provider with Vietnam-based GenAI data-science roles |
RAG or fine-tuning: which should you ask for?
For an enterprise knowledge assistant whose source material changes frequently, RAG is often the more practical starting point: the application retrieves relevant material at answer time instead of relying on a model’s training to contain the latest policy or document. It can support source references and document-level access controls, but it does not guarantee correct answers. Kyanon’s published description is especially explicit about RAG and traceable source grounding (Kyanon GenAI consulting).
Fine-tuning may be useful for response style, formatting, repeated domain patterns or specialized model adaptation. It is not a substitute for a current, permission-aware knowledge source. Both approaches need evaluation, safeguards and monitoring; ask the vendor to justify the method against your use case rather than selling one technique as a universal answer.
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Risks that separate a demo from a production system
- Hallucinations: require citations or source links where appropriate, abstention when evidence is missing, human escalation for consequential decisions and a test set of realistic questions.
- Prompt injection: uploaded files and retrieved pages can contain instructions intended to manipulate a model. Ask how the system treats content as untrusted data and limits tool access.
- Data leakage: prompts, source documents, embeddings, logs and tool outputs each need appropriate access, retention and encryption controls. Clarify whether customer data is used to train a shared model.
- Vietnamese quality: test accents, regional terms, diacritics, names, legal language, code-switching and noisy speech; an English benchmark does not establish local-language performance.
- Weak source documents: scanned PDFs, duplicates, obsolete policies and contradictory versions can undermine retrieval. Identify who will clean, classify and refresh content.
- Lock-in: ask how easily the application can switch model providers, and who owns connectors, prompts, evaluation data and other components. A multi-model claim alone does not establish portability.
- Operating costs: estimate model tokens, retrieval volume, vector storage, hosting, observability and support—not just the initial build.
- Post-launch gaps: assign responsibility for monitoring, content refresh, model updates, security reviews, incident response and user feedback before signing.
Questions to ask before requesting a proposal
- Which generative-AI systems have you deployed to production, and can you provide a reference or anonymized architecture?
- Which models do you support now, and what is the migration plan if a provider changes availability or pricing?
- Can the solution run in our cloud, a private environment or on-premises if required?
- Is our data sent to a model provider, retained, or used to train a shared model?
- How are prompts, documents, embeddings, logs and tool outputs protected and access-controlled?
- How do you evaluate hallucination, retrieval accuracy, prompt injection, data leakage, Vietnamese-language quality, latency and cost?
- Who owns the code, prompts, evaluation datasets, connectors, embeddings and any fine-tuned model?
- What are the expected recurring costs for tokens, hosting, vector databases, monitoring and support?
- What is included in the fixed-price scope, and what will be billed separately?
- Who monitors the live service, refreshes its content and handles incidents after launch?
For a useful initial proposal, prepare the target use case, data sources, likely users and countries, compliance requirements, deployment preference, integrations, desired pilot timeline and budget range. If the need is only a simple public chatbot—or the business has no clean data or technical owner—an off-the-shelf tool may be a better first step than custom development.
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