Reimagining growth.
Vietnam's #1 AI Automation Agency for Practical AI Automation.
Digital Squad designs practical AI systems for marketing, reporting and customer operations, taking a defined Vietnam use case from discovery through adoption. Vietnamese inputs, code-switching, mobile workflows, integrations, human escalation and evaluation are tested before a governed capability moves into production or extends across APAC.
Trusted by 450+ clients over 19 years across APAC
One of Vietnam's Leading AI Development Consultancies — In-House Engineering, Production-Grade Intelligence.
Digital Squad's AI capability is built differently. Unlike agencies that bolt on third-party AI tools, our in-house team of ML engineers and data scientists — holding advanced degrees in machine learning, NLP, and computer vision — design and build production-grade AI systems from the ground up. From intelligent automation and predictive modelling to generative AI applications and AI-powered marketing infrastructure, we translate cutting-edge research into real, measurable business outcomes.
AI creates value in Vietnam when it improves a defined customer or operational workflow and can handle the language, data and systems people actually use. We connect process discovery, data readiness, model choice, integration, security, governance and adoption, giving teams a practical route from prototype to accountable production capability. Vietnam use cases are selected where Vietnamese documents, customer conversations or distributed operations create measurable work that conventional workflow tools cannot address adequately.
From AI-powered content intelligence to computer vision pipelines and custom NLP systems, we build scalable AI capabilities that give organisations a durable, compounding edge — not experiments that never reach production.
Framework-first thinking
We design AI around a real operational or commercial constraint, then connect the use case, data, model, workflow, integration, governance and adoption plan required to solve it. The result is a production system that people can use, leaders can measure and technical teams can operate responsibly as needs and models evolve. Governance keeps performance and responsibility visible after deployment.
Our Vietnam AI framework starts with the workflow and measurable outcome, not a preferred model. Vietnamese and English inputs, data quality, privacy, integration, evaluation, human review and change management are designed together. The result is a system people can operate, govern and improve after launch. Evaluation design includes native-language inputs, code-switching and operational edge cases, while permissions and integration choices reflect the systems local teams actually use rather than a clean demonstration environment.
In-House AI Engineering Capability
We maintain a dedicated AI engineering team—not outsourced contractors. Our engineers hold advanced degrees in machine learning, data science, and software engineering, with production experience deploying AI systems at scale. This in-house capability enables rapid iteration, quality control, and knowledge continuity throughout development cycles—delivering solutions faster and more reliably than fragmented vendor arrangements.
Business-Outcome Focused AI Strategy
AI projects fail when technology leads strategy. We begin with business outcomes—identifying specific problems, quantifying improvement opportunities, and designing AI solutions that deliver measurable value. Our discovery process evaluates technical feasibility, data requirements, integration complexity, and ROI projections before development begins—ensuring investments focus on high-impact applications rather than experimental technology.
Full-Stack AI Development & Deployment
We handle complete AI solution lifecycles: strategy and discovery, data engineering and preparation, model development and training, integration and deployment, monitoring and optimisation. Our MLOps infrastructure ensures models perform reliably in production environments, with automated retraining pipelines, performance monitoring, and continuous improvement protocols. Solutions deploy on cloud platforms, on-premises infrastructure, or hybrid architectures based on security and compliance requirements.
Generative AI Integration Expertise
Vietnamese documents, customer conversations and operational data can expose limitations hidden by an English-only prototype. We include representative language and edge cases in evaluation, define where human review remains mandatory and make regional system dependencies visible before development. Accuracy and adoption are production requirements. Monitoring covers language-specific failure patterns as well as overall model performance. Mobile and messaging interfaces may be more practical than a standalone portal for frontline adoption, so channel constraints and escalation to a human owner are decided before model selection.
We turn practical AI opportunities in Vietnam into governed systems people can use, with adoption and operational value visible from the start.
Phase 1
AI Discovery & Feasibility Assessment
We map the Vietnam workflow, users, Vietnamese and English inputs, data, systems, decisions, risks and baseline performance. The assessment identifies where AI can create measurable value, which integrations and approvals are required, and what human review must remain before development is prioritised. Representative Vietnamese records and user interviews test data quality, exception frequency and the cost of errors, allowing leaders to reject attractive prototypes that lack a reliable production case.
Outcome: AI opportunity assessment with feasibility analysis, ROI projections, and implementation roadmap.
Phase 2
Proof-of-Concept Development
We build rapid prototypes demonstrating AI solution viability using sample data—validating approach before full development investment, then design the product and delivery architecture, including integrations, evaluation, guardrails and the decisions that remain human-owned.
Outcome: Working proof-of-concept demonstrating technical feasibility and expected performance.
Phase 3
Production Development & Integration
We develop production-grade AI solutions with full data pipelines, model training, testing, and integration with existing systems, then build and test the priority capability with representative data, user feedback and clear failure handling.
Outcome: Deployed AI solution operating in production environment with monitoring infrastructure.
Phase 4
Optimisation & Continuous Improvement
Production monitoring covers accuracy by language and use case, task completion, exceptions, latency, cost, adoption and the business measure the system should improve. Feedback from Vietnam users informs models, prompts, interfaces and workflow changes while governance records preserve accountability. Monitoring separates language accuracy, retrieval quality, completion and human override rates, then relates those signals to cycle time, service quality, adoption or revenue improvement.
Outcome: Sustained AI performance improvement with ongoing adaptation to changing conditions.
Trusted by people just like you.
Anvesh Katuri
Founder at Hyperios
“From branding to execution, the team delivered clarity and strategy beyond expectations. Within 3 weeks we ranked on page one across 7 markets for competitive generative AI terms.”
Ben Tan
KrisShop, Singapore Airlines
“After years of struggling with negative ROAS across multiple agencies, this was the first team that actually turned things around. The difference was night and day.”
Joanna Du
Head of Marketing at Cahoot
“The team brought strategic clarity we had never experienced before. Their SEO insights reshaped our entire content approach and quickly lifted visibility across our core programmes.”
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FAQ
Frequently asked questions
Practical answers for organisations applying AI to measurable operations in Vietnam.
We can scope agents, copilots, retrieval and knowledge assistants, document workflows, classification or prediction systems, automation pipelines, data connections and integrations around a defined operational need. Existing models such as GPT, Claude or Gemini may be part of the solution, alongside custom orchestration or machine learning where the case warrants it. Vietnamese inputs, code-switching, mobile or messaging workflows and the people who own the outcome shape the design. We define the task, evidence, user, escalation point and measurable improvement before choosing the technical pattern.
Both routes can be appropriate. Many problems are better solved by a controlled pipeline around an existing model than by training from scratch; custom retrieval, interfaces, evaluation, orchestration or machine learning may be justified when data, workflow or differentiation needs more precision. We compare capability, data availability, cost, integration effort, permissions and ongoing ownership before recommending an approach for a Vietnam team. The decision also considers how the system will change when sources, prompts, models, language or customer expectations change after launch.
Consulting clarifies the business problem, workflow, data readiness, risks, architecture, business case and delivery sequence. Development turns that decision into a tested, integrated system with evaluation, monitoring, documentation and adoption support. Keeping the stages connected means the Vietnam strategy is grounded in what can actually be built, deployed and operated, rather than recommending a compelling demonstration with no owner or production route. It also makes trade-offs visible to leadership, delivery teams, subject experts and the people who will handle exceptions.
A focused proof of concept commonly takes four to eight weeks when the use case, data and people are available. A production system with integrations, MLOps, monitoring, evaluation and workflow adoption commonly takes three to six months, depending on complexity and accuracy requirements. We set the sequence before development, expose dependencies and use the first cycle to decide whether the evidence supports production investment rather than promise a generic launch date. That cycle should show what improves, what remains uncertain and which Vietnam team will own the next stage.
Yes. We assess the existing data, systems, permissions, people and workflow, then identify what must be made reliable before a model is useful. Foundational pipelines or integrations can be built around a contained use case instead of an abstract platform. Workshops, documentation and handover help a local team understand how to operate the capability, while Vietnamese examples and exceptions make adoption practical after launch.
Production readiness includes model versioning, evaluation against representative tasks, monitoring, drift or failure detection, retraining decisions, access controls, data minimisation, anonymisation where appropriate and API security. We map Vietnam data flows alongside regional integrations and agree human escalation when output is uncertain. Applicable data-protection requirements are considered for the operating markets, but we document the actual responsibilities, assumptions and controls rather than offering a vague compliance promise.
We start with a scoping workshop to define the problem, desired decision, data, users, exceptions and success measure, then check whether AI is the right approach. The work moves through proof of concept, evaluation, production development, testing, deployment and handover. Documentation, monitoring dashboards, ownership and a feedback route are included so a Vietnam team receives an inspectable capability rather than a black box.
We support marketing and content operations, customer service, knowledge management, document workflows, data analysis, professional services and workflow automation when the use case has a measurable case. We start with one accountable process, define the source data, permissions, exception handling and success measure, then test with Vietnamese inputs, code-switching, mobile or messaging needs and real operational edge cases. Owners review accuracy, adoption, cycle time, service quality and revenue or capacity effects before expansion. The first release is deliberately bounded; a successful demonstration alone does not justify production scale or a broad industry promise.