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TecAce가 AX Diagnostic을 만든 이유와, 그것으로 무엇을 알 수 있는지에 대하여

"You want to put AI on a smartphone?" — The Beginning of a Reckless Challenge The story of how TecAce's AI Supervision Team built the On-Device LLM Tester "Um… I'd like to automatically measure LLM performance on a smartphone." A brief silence fell over the meeting room. Our team was developing our own on-device AI chatbot. The problem was that every time we swapped models, a tester had to physically hold the phone, send prompts one by one, and time everything by hand. That p

DESIGNER IS NOT DEAD In the AX Era, Design Is About Standards, Not Tools Figma or Adobe is not the real issue. In an era where AI tools are becoming part of everyday work, the more important question is this: can your brand and design standards remain consistent even when the tools change? Executive Summary As AI tools continue to grow, questions like “Can we design without Figma?” or “Do we still need Adobe?” have become more common. But these questions miss the bigger point

Gartner says 85% of AI failures come down to data-quality problems. Here are the 3 failure patterns repeatedly seen in SMBs and mid-sized companies in Korea and the U.S., plus a 20-item self-diagnosis checklist to run before adoption. Of these, only 8 truly require humans.

AI must work even where there is no internet. TecAce On-device combines OTA updates with a hybrid offline/online architecture to deliver up-to-date knowledge anywhere — from factory floors out of signal range to campsites deep in the mountains. In this article, we explore five industry use cases where the TecAce platform can be applied, each illustrated with a concrete scenario. Case 1. An AI Manual Companion for Field Workers Field AI Companion — Factories · Shipyards · Plan

Design is no longer only something you make with a mouse. It is becoming something you shape through conversation with AI. Executive Summary: The Future of Design Conversational design tools like Claude Design are revolutionizing how we approach design. Work no longer begins with a blank canvas. Instead, a user describes their needs, and AI generates the first version. This initial output is then refined through conversation, comments, and direct edits. This shift has the pot

Artificial intelligence is evolving fast. One of the most exciting developments is the rise of efficient AI language tools that operate directly on devices. This shift changes how businesses handle data, privacy, and speed. Instead of relying solely on cloud servers, AI can now run locally on smartphones, laptops, or edge devices. This blog dives into the world of on-device large language models, explaining what they are, why they matter, and how they can transform enterprise

Executive Summary AI-powered meeting summaries are nothing new. TecAce Software had already been using various solutions to boost productivity through automated meeting recaps. But two persistent pain points remained: every attendee received the same summary regardless of their role, and recurring meetings lacked the continuity needed to surface meaningful insights. To solve this, the team built an internal Personalized AI Meeting Note System — integrating Speaker Recognition

Why This Test Matters One SoC generation changed inference speed by 29%. We ran Gemma3 270M INT8 on four Galaxy A-series devices to find out where on-device LLM becomes practically usable. We tested gemma-3-270m-it-int8 via MediaPipe CPU backend on the Galaxy A16, A26, A36, and A56, measuring latency, token throughput, memory, and accuracy across 25 prompts. We also compared parallel (all 4 devices simultaneously) vs serial (each device independently, 2 runs) execution to ver

The Future of On-Device AI and TecAce's Roadmap Throughout this 9-part series, we have chronicled the entire journey of developing an on-device chatbot—a solution to cloud cost and data security issues. We covered everything from selecting a Small Language Model (SLM) and applying quantization, integrating offline STT/TTS, building local RAG, to rigorously validating quality using AI SuperVision and overcoming hardware performance constraints. In this grand finale, Part 10,

Challenging Performance Limits Heat, Battery, and Response Speed In Part 8, we shared how we caught hallucinations and improved response quality using 'AI SuperVision'. While making the model smarter and more accurate is a huge milestone, running it in a real-world smartphone environment (like the Galaxy S25 FE) forces us to confront harsh physical walls: Thermal management, Battery consumption, and Latency limits. Unlike the limitless resources of cloud data centers, a mob

Catching Hallucinations Analyzing SuperVision Test Results In Part 7, we built an automated testing pipeline that bridged our on-device chatbot app inside a smartphone with the AI SuperVision server on a PC. This enabled an end-to-end flow from prompt injection and answer extraction to automated grading. We finally had an environment capable of running dozens of test cases automatically. So, what kind of report card did our on-device SLM (Gemma-2B based) receive from these

Building SuperVision An Automated Chatbot Testing Pipeline In Part 6, we explained the background of introducing Testworks' 'AI SuperVision' tool to objectively evaluate the chronic hallucination issues inherent in generative AI. However, to actually apply this tool to our project, we had to overcome a significant technical barrier. Our LLM chatbot operates completely offline "On-device" (inside a smartphone), whereas the AI SuperVision system evaluating it exists in a "PC

How to Verify AI Quality? Introduction to SuperVision In Part 5, we explored how to inject our company's proprietary knowledge into the on-device chatbot using Local RAG (Retrieval-Augmented Generation) and Multi-Context Switching. However, equipping the chatbot with knowledge does not immediately solve all problems. "How can we be absolutely sure that this chatbot isn't fabricating answers and is truthfully speaking only about what is in the provided documents?" In Part 6,

The Ears and Mouth of a Chatbot On-Device STT/TTS Integration In Part 3, we explored the optimization process of compressing a massive language model to fit the constrained resources of a smartphone and boosting inference speed using the mobile NPU. Now that we have successfully embedded a fast and smart "brain" inside the device, it is time to give our chatbot the "ears and mouth" it needs to interact naturally with users. In a mobile environment, typing out long texts eve

A Chatbot That Understands Context Implementing Local RAG and Multi-Context Switching In Part 4, we gave our chatbot "eyes, ears, and a mouth" by integrating on-device STT and TTS. However, no matter how well a chatbot listens and speaks, it is only half-useful as a business assistant if it doesn't know your specific "domain knowledge"—like internal company regulations or specific product manuals. Because Small Language Models (SLMs) are compact, they do not perform as well

Core Technologies of Mobile AI Quantization and NPU Optimization In Part 2, we discussed our selection of Gemma-2B as the ideal Small Language Model (SLM) for our project and shared our experiences benchmarking CPU and GPU performance in a constrained smartphone environment. However, the initial tests revealed significant challenges: noticeable latency delays and out-of-memory errors. To run LLMs in real-time on a mobile device held in the palm of your hand—not on a data ce

Giant Language Models in the Palm of Your Hand Mobile SLM Selection Strategy In Part 1, we explored how "On-Device AI" is becoming an essential paradigm for solving cloud cost and data security issues. But how can we fit massive Large Language Models (LLMs) with tens or hundreds of billions of parameters—which typically run on massive GPU racks in data centers—into a small smartphone? The answer lies in Small Language Models (SLMs). In Part 2, we will compare the most notab

Why "On-Device AI" Now? Over the past few years, generative AI, led by services like ChatGPT, has revolutionized our daily lives and workflows. However, behind these powerful AI services lies a common limitation: cloud dependency. The standard architecture—where user queries are sent to cloud servers and the computed results from massive data centers are sent back—inevitably introduces risks such as data privacy breaches, network latency, and exorbitant server maintenance co

Not Working Alone, But Working 'Together' Through the past four posts, I’ve shared my raw experience with AX Pro, from deployment to real-time operations. As a PO and brand owner, I’ve used countless tools, but few focus as deeply on the essence of 'Human-AI Collaboration' as AX Pro. I didn't just hire a machine that answers questions; I gained a 'Growth-oriented Teammate' that understands our philosophy and evolves under expert guidance. AX Pro main page Three Decisive Mom