iApp's NLP System
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When a bank processes thousands of loan applications overnight without human intervention, when an e-commerce platform automatically extracts data from invoices and updates inventory, or when a call center transcribes customer conversations in real-time — that's Automation at work. It's the technology that lets machines do repetitive work so humans can focus on what matters most.
When a customer writes "สินค้าดีมาก ส่งเร็ว ประทับใจ!" (Great product, fast delivery, impressed!) in a review, how does your business automatically know this is positive feedback? When thousands of social media comments flood in after a product launch, how can you quickly understand whether people love it or hate it? The answer is Sentiment Analysis — one of the most practical applications of AI in business today.
Have you ever chatted with customer support on a website at 2 AM and gotten an instant reply? Or asked a voice assistant to play music or check the weather? Chances are, you were talking to a chatbot. These AI-powered conversational agents are transforming how businesses interact with customers, and they're becoming smarter every day.
When your phone recognizes your face in a split second, when Google Translate converts Thai to English almost perfectly, or when ChatGPT generates human-like responses — that's Deep Learning at work. It's the technology behind virtually every major AI breakthrough of the last decade, and it's transforming what computers can do.
Humans naturally understand the world through multiple senses - we see images, hear sounds, read text, and watch videos simultaneously. But until recently, AI systems were limited to processing only one type of data at a time. Enter Multimodal AI - artificial intelligence that can understand and work with multiple types of data together, just like humans do.
By Dr. Kobkrit Viriyayudhakorn, CEO & Founder, iApp Technology
You've probably heard of ChatGPT, Claude, or Google's Gemini. These are all powered by something called a Large Language Model or LLM. But what exactly is an LLM? How does it work? And why should you care?
In this beginner-friendly guide, we'll explain everything you need to know about LLMs in simple terms - no PhD required!
By Dr. Kobkrit Viriyayudhakorn, CEO & Founder, iApp Technology
How does your phone understand when you type in Thai? How can AI analyze thousands of customer reviews in seconds? How do chatbots know how to respond to your questions? The answer is Natural Language Processing or NLP. In this guide, we'll explain everything you need to know about Thai NLP in simple terms.
By Dr. Kobkrit Viriyayudhakorn, CEO & Founder, iApp Technology
One of the most common questions we hear from Thai AI engineers and technical teams is: "Should I use RAG or fine-tuning for my Thai language application?" It's a critical question that directly impacts development costs, performance, maintenance complexity, and long-term scalability.
The answer, as with most engineering decisions, is: it depends. But understanding when to use each approach—and increasingly, how to combine them—can mean the difference between a successful AI deployment and an expensive failure.
This article provides a comprehensive technical comparison of Retrieval-Augmented Generation (RAG) and fine-tuning specifically for Thai language applications, drawing from our experience at iApp Technology deploying both approaches across hundreds of Thai enterprises.
If there is one technology that can create a major change to our world, it must be NLP.
NLP or Natural Language Processing is one type of AI that can understand human speech and text. We can describe the overall picture of NLP as the 'core' of all forms of communication that occur on computers.
How useful is NLP to us today? Just opening Google to search for restaurants near me, you will get the information immediately. Or even Smart Home that is controlled by voice to turn on devices in the house. These technologies receive commands from us to analyze the results to best suit our needs. This is the power of NLP.