Instructor-led IT courses across web, mobile, data, design and more.
Every organisation produces data every day from many sources: revenue, invoices, receivables, budgets, utility costs for each site, service tickets and contracts stored as PDFs. Finance teams have to close the books and chase receivables, while IT and data…
Many organisations have already experimented with AI, yet a lot of projects never get past the demo, because the people who understand the business problem and the people who can build the system sit on opposite sides. A Forward Deployed Engineer (FDE) closes…
As organisations build more agents in Copilot Studio, a single agent that handles everything starts to answer outside its scope, pull knowledge from the wrong source and become harder to maintain with every new capability. The answer is to split the work…
Document work in many organisations still relies on people keying in data: invoices, receipts, request forms and customer emails that have to be read and sorted one by one. Many teams already use Power Automate and Power Apps but do not realise that AI…
Content and digital media teams now have data pouring in from many platforms at once: Facebook, Instagram, YouTube, TikTok, LINE and their own websites. Each platform exports files in a different layout and counts its metrics differently, so most of the time…
Almost every development team now uses ChatGPT to write code, explain existing code and track down problems, but the results are still inconsistent, and sometimes the code looks right but does not work. At the same time, organisations want to build LLM…
Sales and account teams in service, maintenance and project businesses handle a heavy daily workload: following up service work for customers with many sites, preparing quotations and scopes of work, summarising site survey meetings, answering emails and…
AI coding agents are no longer just tools that complete the next line of code. They behave more like a teammate who reads the whole project, plans, edits many files, runs commands and opens pull requests on their own. Many developers have started using them,…
AI tools at work are moving from chatbots that answer one question at a time to AI agents that plan multi-step work, use connected files and apps, produce documents, spreadsheets and reports, and run repeating jobs on a schedule, in both ChatGPT and Claude.…
Many organisations already use Claude every day, but the results still depend on who is giving the instructions. Each person has to type long prompts explaining the steps, the document format and the team's standards every single time, so the output is…
Almost every employee can now use AI tools for free, including Claude, ChatGPT, Gemini and Microsoft Copilot. Yet many people still use AI only for general questions, get answers that do not fit the task, rewrite them several times, are unsure which tool…
Many developers already use Claude Code fluently as a coding assistant, one prompt at a time. As the work grows, though, a single agent's context fills up, tasks that could run in parallel are done one after another, and results depend on who writes the best…
Many development teams already use AI to help write code, but mostly for line-by-line completion or questions in a chat window. Codex works as a coding agent: it reads the whole repository, runs commands, edits several files and hands work back as a diff or a…
Many employees already know about AI and have asked it the odd question, yet it has not become part of their everyday work. They are unsure which tasks to start with, what data they may or may not enter, and how far the output can be trusted, so AI gets used…
Car rental businesses today compete on convenience and how quickly they respond. Most customers browse promotions on their phones, message the shop on LINE and expect to book, pay and get a confirmation straight away, even outside opening hours. Many owners…
A great deal of office time still goes on gathering figures, building tables and charts, writing reports and preparing management slides every month, even though today's AI tools can analyse data and create Office files directly. Most users still get results…
RAG systems built on vector search alone answer well when the answer sits in a single passage, but they often miss when a question has to connect several documents, such as which product models a customer uses that are affected by the latest notice, or when…
Calling an LLM API and getting an answer back takes only a few lines of code, but building an application that works in practice is another matter. Developers have to deal with answers in unpredictable formats, runaway costs, rate limits, data the model does…
LINE Official Account is the main channel Thai customers use to contact shops and businesses, yet admins struggle to keep up with questions about prices, stock, order status and the same queries over and over. Older bots that reply by keyword or intent can…
Many customers still pick up the phone when something is urgent, yet call centres struggle to answer in peak hours and staff repeat the same answers all day. The latest voice AI speaks Thai far more naturally and replies almost instantly, but building a voice…
Once staff in every department start using AI, organisations tend to hit the same problems. Each team signs up for a different service and holds its own API keys, nobody knows the total cost, customer data or confidential documents can be sent out by…
Good prompts and RAG solve a lot of problems, but some tasks need more. You may want a model that always answers in your organisation's format, uses domain terms correctly, classifies Thai documents accurately, or lets a small in-house model replace a large…
Every organisation has documents that someone re-keys every day: invoices, tax invoices, receipts, application forms and contracts, often arriving as scanned PDFs or phone photos. Traditional OCR struggles with Thai and has no idea which number is the net…
Many organisations want to use LLMs on internal documents, customer data or source code, but the rules say that data must not leave for an outside provider. The answer is to run open-weight models on the organisation's own machines or servers. Once a team…
