AI Automation
Intelligent automation solutions powered by AI and machine learning to optimize business processes
Get StartedAI Automation | From pilot to production—with governance and ROI tracking
Deliver measurable business value—not just POCs
ARIS Vietnam helps you identify high-impact automation opportunities and implement production-ready AI solutions. We don't just build models—we integrate them into your workflows, ensure data quality, establish monitoring, and measure ROI. Our approach covers the full lifecycle: from use case discovery and model development to deployment, governance, and continuous improvement.
Common AI automation challenges
- •Unclear business case; AI becomes a technology experiment
- •Poor data quality and availability
- •Lack of integration with existing systems
- •No monitoring or model performance tracking
- •Security and compliance concerns (data privacy, AI ethics)
- •Low user adoption due to lack of trust or training
- •Unrealistic expectations about AI capabilities
Why AI projects fail
- ⚠No baseline metrics or success criteria defined
- ⚠Insufficient or biased training data
- ⚠Model drift not monitored in production
- ⚠No fallback mechanisms for model failures
- ⚠Integration complexity underestimated
AI Automation Delivery Process
Free Consultation & Use Case Discovery
1-2 weeksWe assess your processes, identify automation opportunities with high ROI potential, and propose a proof-of-concept or full implementation roadmap.
Feasibility Study & Data Assessment
2-3 weeksEvaluate data availability, quality, and infrastructure requirements. Define success metrics, baseline performance, and technical constraints.
POC Development & Validation
4-6 weeksBuild a working prototype with real data, validate accuracy and performance, and demonstrate business value with stakeholders.
Production Development & Integration
8-12 weeksDevelop production-grade solution with proper error handling, API integration, security controls, and scalability considerations.
Deployment & User Training
2-4 weeksDeploy to production environment, conduct user training, establish monitoring dashboards, and create operational runbooks.
Monitoring & Continuous Improvement
OngoingTrack model performance, user adoption, and business impact. Refine models based on production data and evolving business needs.
Scope of Services
・Use case discovery and ROI analysis ・Data preparation, cleansing, and annotation services ・Custom ML model development (classification, prediction, NLP) ・Intelligent document processing (OCR, form extraction, invoice processing) ・Process automation with AI integration (RPA + AI) ・Chatbot and virtual assistant development ・API development and system integration ・Model monitoring, retraining, and MLOps setup ・Training and knowledge transfer
Use Cases
Intelligent Document Processing
Automate invoice processing, contract analysis, or form data extraction with AI-powered OCR and NLP. Reduce manual data entry by 80%+.
Customer Service Automation
Deploy AI chatbots and virtual assistants to handle routine inquiries, ticket classification, and customer routing—improving response time and satisfaction.
Predictive Analytics & Forecasting
Build ML models for demand forecasting, churn prediction, maintenance prediction, or sales forecasting to enable proactive decision-making.
Process Mining & Optimization
Analyze workflow logs with AI to identify bottlenecks, automate decision points, and optimize business processes.
Quality Inspection & Anomaly Detection
Implement computer vision and ML models for automated quality control, defect detection, or security monitoring.
Why ARIS
- ✓Business-first approach: We focus on ROI, not just technology
- ✓Production-ready implementations with proper monitoring and governance
- ✓Data quality and model performance tracking from day one
- ✓End-to-end delivery: from feasibility to deployment and continuous improvement
Ready to automate with AI?
Let's discuss your automation opportunities and create a roadmap for AI implementation.
Schedule a ConsultationFrequently Asked Questions
Good candidates for AI automation typically involve: repetitive tasks with high volume, tasks requiring pattern recognition or prediction, processes with clear rules but complex logic, or activities that benefit from natural language understanding. We offer free consultation to assess your specific use cases.
Data requirements vary by use case. Generally, you need: historical examples of the task, sufficient volume for training (hundreds to thousands of examples), labeled data or ability to label, and data that represents current and future scenarios. We can help assess your data readiness and create a data preparation plan.
A POC typically takes 2-4 weeks. Production implementation depends on complexity: simple document processing (4-6 weeks), chatbots (6-8 weeks), complex ML models (2-3 months). We provide phased delivery for faster time-to-value.
We implement comprehensive monitoring: track prediction accuracy, detect model drift, monitor data quality, alert on anomalies, and establish retraining schedules. We also design fallback mechanisms for edge cases and provide regular performance reports.
We follow responsible AI principles: transparent decision-making, bias detection and mitigation, data privacy compliance (GDPR, local regulations), secure data handling, and human oversight for critical decisions. All implementations include documentation of model behavior and limitations.
Yes. We specialize in integration via REST APIs, message queues, database connections, or RPA tools. We work with your existing tech stack (ERP, CRM, databases) and ensure seamless workflow integration without disrupting operations.
Yes. We provide: end-user training for AI tools, technical training for your IT team, documentation and runbooks, ongoing support during adoption, and knowledge transfer sessions. We ensure your team can maintain and improve the solution independently.
We set clear success criteria upfront and validate through POC before production. If accuracy is below target, we: analyze failure patterns, improve training data, adjust model architecture, or recommend hybrid approaches (AI + human review). We only proceed to production when success criteria are met.
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