Cloud, DevOps, data and practical AI
We move systems to the cloud, automate how they are built and released, and turn their data into reports and AI features such as document reading and search, with the security and cost controls an organisation needs.
Services in this pillar
Each service has its own page with scope, approach and buyer questions.
Cloud Migration & Architecture
AWS architecture and migration for existing systems, planned per workload around cost, data location and a safe cutover.
DevOps & CI/CD
Build and deployment pipelines, infrastructure as code and monitoring, set up so your own team can run them.
Data & Analytics
Data pipelines, a shared data model and reports or dashboards built around the decisions your teams make.
AI & Document Intelligence
Extraction from documents, search over internal files and classification of incoming work, with human review where errors matter.
How the four services connect
These services share one dependency: knowing what your data is, where it lives and whether it can be trusted. A cloud migration brings data problems to the surface, reporting depends on clean data, and an AI feature repeats whatever errors the data contains.
We usually work in that order. First a platform that is stable, monitored and costed per service. Then data that is reconciled and has an owner. Then AI applied to one narrow task where you can measure whether the output is right, such as extracting fields from a known type of document. The reporting system for GMM Grammy's accounting, with more than 30 reports from one datamart, is an example of the middle step.
What buyers ask about cloud, data and AI
Will moving to the cloud reduce our costs?
Not by itself. Savings come from resizing workloads so they scale down when idle, replacing self-managed components with managed services, and tagging resources so each team sees its own spend. Moving servers across unchanged often costs more per month than the data centre did. We model the expected bill before you commit.
Is our data good enough for AI?
We check it against the specific task, since data can be fine for one use and unusable for another. Common problems are fields that mean different things in different systems, missing history and poor-quality scans. Where the data is not ready, fixing it becomes the first phase of the project.
How do you handle PDPA and data residency?
We decide early where data is stored, which processing happens in Thailand, what leaves your environment and under which controls, and we build the technical measures for those decisions. Interpreting your legal obligations is a matter for your legal advisers.
Can we use commercial AI models with sensitive data?
Sometimes. It depends on how the data is classified and on the provider's contract terms, for example whether your inputs may be used for training. The alternatives, such as a model hosted in your own environment or a design where sensitive fields never leave it, cost more and can be less capable. We compare the options with you at the start.
Next step
Start with a conversation
Tell us about the problem you are trying to solve. A senior person reads every inquiry and replies with our view of fit, scope and cost, including when another option would suit you better.