What are you building?
What are you building?
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Members & CommunitySocial EntertainmentDigital EducationMember App DevelopmentMember cards, points and coupons, synced with POSBooking & Registration SystemsOnline booking, scheduling, check-in and eventsData from production equipment, sensors and cameras goes into one platform with real-time dashboards and alerts, and AI finds defects in images and signals and predicts anomalies. We start with one line or one equipment type, prove it on real data, scale what works, and connect it to your existing MES and ERP.
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AIoT means handing the data collected from connected equipment (IoT) to AI, so defects, anomalies and energy waste are caught in real time or even predicted. IoT reliably brings data from equipment, sensors and cameras into a platform; AI recognises patterns in those images and signals. You do not need to change the whole factory at once. Pick one line or one equipment type and one measurable problem, such as missed defects, unplanned downtime or energy per unit, and run a PoC on real data to measure accuracy and benefit. Once the numbers hold up, extend to other lines and connect to your existing MES and ERP. Every step then has results you can check.
Real projects AI and IoT work
Below are AI and IoT systems we built for a research institute, equipment makers and manufacturers. Some started with a single recognition target; others we continue to maintain and extend after launch.
National Atomic Research Institute (NARI)
Commissioned to develop an AI-assisted inspection system that automatically identifies defects in eddy current inspection signals from nuclear power plant equipment, improving inspection efficiency and consistency.
Since 2023Major electronic test and measurement company
AI tool software developed for a major electronic test and measurement equipment maker.
2026Aquaculture operator
A smart management platform for assessing broodstock shrimp gonad maturity and gestation, now maintained by us.
Since 2025HVAC and ventilation equipment maker
An app for a clean-air ventilation system, later extended with an admin back office and APIs, plus a partner-brand version of the app and yearly maintenance.
2026Delta Electronics
An IoT app system for power and electronics products.
2023In-vehicle equipment company
An equipment logging system built for an in-vehicle equipment company.
* Projects without a client name are described by industry under confidentiality agreements.
Scope What AIoT development covers
How accurate AI is depends on how reliably data is collected and how well it is labelled. So we start at the equipment and sensors and carry through to models in production and links to existing systems.
Data from PLCs, sensors, instruments and cameras enters the platform through gateways or edge hosts; it is buffered during outages and resent afterwards.
Temperature, current, vibration and run status shown live, with the owner notified on threshold breaches or abnormal trends and the response logged.
Visual defects, abnormal measurement signals, animal or crop condition; trained on real site data with accuracy and miss rates measured.
Data from different machines and brands in one platform, searchable by line, site and time, with energy and utilisation reports generated automatically.
Historical data used to predict equipment faults, consumable replacement and energy use, helping tune parameters and schedules.
Recognition results and equipment data written back to your MES, ERP or quality system through APIs, with no extra copy of the data.
Compare Equipment monitoring vs AIoT platform vs packaged MES
They solve different problems and often coexist. Most factories already run an MES or ERP; an AIoT platform adds the equipment data and AI layer.
| Aspect | Equipment monitoring only | AIoT platform | Packaged MES |
|---|---|---|---|
| Problem solved | Equipment monitoring only: See equipment status and readings | AIoT platform: Find defects and anomalies in data, and predict them | Packaged MES: Work orders, scheduling, reporting and traceability |
| Data scope | Equipment monitoring only: One machine or one brand | AIoT platform: Mixed-brand equipment, sensors, images and existing systems | Packaged MES: Mainly production process data |
| AI | Equipment monitoring only: Usually none | AIoT platform: Image and signal recognition, prediction and optimisation, trained on site data | Packaged MES: Depends on the product, mostly report analysis |
| Flexibility | Equipment monitoring only: Whatever the equipment vendor offers | AIoT platform: Built around your lines and problems | Packaged MES: Follows the package's workflow; customisation is costly |
| Existing systems | Equipment monitoring only: Usually standalone | AIoT platform: Connects to MES and ERP, adding the data and AI layer | Packaged MES: Becomes the production core; replaces or integrates legacy systems |
| Best for | Equipment monitoring only: Few machines, seeing data first | AIoT platform: A clear quality, downtime or energy problem to solve | Packaged MES: Building plant-wide production management |
Process How we deliver
The most common AIoT failure is buying lots of hardware before confirming the data is available and the AI is accurate. Every phase ends with something you can accept.
2–4 weeks
On site we check equipment models, interfaces and existing systems, choose one line or equipment type and one measurable target, and obtain the first batch of real data.
2–4 months
Models are trained and tested on real data, measuring accuracy, miss rate or alert hit rate against the current method before deciding whether to scale.
5–8 months
Extend to more equipment and lines, build the data platform, dashboards and alerts, put models into production and connect MES, ERP and other systems.
Ongoing
Monitor data collection and recognition results; when lines, materials or lighting change, collect misclassified samples and retrain. The same team stays responsible.
Pricing Price ranges
Estimates based on custom system projects in Taiwan in 2026. The final figure depends on the number of devices and their interfaces, the difficulty of the AI target and the systems to connect.
| Scope | Price range | Typical content | Timeline |
|---|---|---|---|
| Site review and planning | NT$100k–300k | Review of equipment, data sources and existing systems, choice of validation target, and a comparable spec | 2–4 weeks |
| PoC validation | NT$500k–1M | One line or equipment type and one AI or monitoring target, validated on real data | 2–4 months |
| Rollout and integration | NT$1M–2.5M | Multi-device data platform, dashboards and alerts, AI models in production, MES and ERP connections | 5–8 months |
| Platform | NT$2.5M and up | Multiple sites, multiple models, prediction and optimisation | 8 months and up |
Sensors and hardware, edge computing hosts, cloud and GPU costs are billed by actual usage. Post-launch maintenance and model updates are a separate agreement.
FAQ Common questions
Based on custom projects in Taiwan in 2026: site review and planning is about NT$100k–300k; a PoC on one line or equipment type is about NT$500k–1M; a multi-device data platform with dashboards, AI models in production and MES/ERP connections is about NT$1M–2.5M; a multi-site, multi-model platform is NT$2.5M and up. Sensors, edge hosts, cloud and GPU costs are extra.
About 3–5 months from site review to PoC results: 2–4 weeks for the review and 2–4 months for the PoC. Scaling and integration after a successful PoC takes about 5–8 months, and a multi-site platform 8 months or more. Timing depends mostly on how quickly data can be obtained and how open the equipment and system interfaces are.
Yes. We handle data collection, model training, system development and model updates after launch. What we need from you is staff who know the site, to explain the judgement criteria, confirm labels and review the numbers with us during the PoC.
Collect data first, then build AI. The site review checks the volume and quality of existing data; if it falls short, we add sensors or cameras and set up a labelling process, while threshold alerts solve part of the problem in the meantime. The PoC starts once there is enough data to train on.
Yes. We do not replace your MES or ERP; we build the equipment data and AI layer and write recognition results, alerts and equipment data back through APIs. Without an API, database or file exchange can work, but reliability and maintenance cost are assessed first.
It happens, so we plan for it. New materials, fixtures or lighting can lower accuracy. We monitor recognition results, collect misclassified samples, relabel and retrain, and update the model after validation; this is written into the post-launch maintenance agreement.
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Get started Start with one line
No full spec needed. Tell us which line or equipment type, what data you have today, and whether the goal is fewer defects, less downtime or lower energy use.