AI Applications
Bring AI into your existing product and workflows.
We build AI features on top of your existing systems: chat assistants, document and image processing, and Q&A grounded in your own knowledge base — solving repetitive work with your data.
How it works
We start from the task and the data you have, choose the right model and deployment (cloud or on-premise), then wire it into your interface and workflow — shipping a working feature, not a demo.
What this covers
- Chat assistants and conversational agents (LINE AI support)
- Document parsing, extraction and classification
- Image recognition and processing
- Knowledge-base retrieval Q&A (RAG)
- Model APIs — OpenAI, Claude, Gemini
- Integration with your existing systems and APIs
Where AI can plug in
AI isn’t about replacing your whole system — it’s about handing over the most labour-hungry step. These are the most common entry points; start with one feature and see results within weeks.
- AI 智能客服
- 商品推薦引擎
- AI 智慧搜尋
- 圖片辨識與標記
- 語音轉文字紀錄
- 行銷文案生成
- 表單與單據自動判讀
- 多語即時翻譯
- Start with one featureNo company-wide rollout required. Pick the single most labour-intensive step — say, front-line support or data entry — prove it works, then expand.
- Built into what you already runNo rebuild needed: the AI feature embeds straight into your existing site, app or back office — staff and customers keep the interface they know.
- Accuracy comes from your dataAnswers are grounded in your product data, FAQs and records — cited, not improvised. Anything uncertain hands off to a human instead of guessing at your customers.
- Results you can measureBefore-and-after numbers: response times, conversion rate, manual workload — every gain is measurable, so the value is plain to see.
Data too sensitive for the cloud? The whole stack can run on your own servers — nothing leaves the building.See Private AI Infrastructure
Example project
APAI Applications
Internal knowledge-base assistant
Turning scattered internal docs into instant answers.
FAQ
Usually just what you already have: FAQs, product data, SOPs, past support logs. We handle the organising and indexing — no pre-labelling on your side.
Answers are grounded in your data (RAG) and cited; anything uncertain hands off to a human. You can also run it internally first and open it to customers once proven.
No. Cloud APIs get you live fastest, but the whole stack can also run on your own servers (see Private AI) — choose by data sensitivity.
A single use case — support Q&A or document processing, say — runs about 3–6 weeks including data preparation, indexing and tuning. Integrating several existing systems takes longer, but still ships in stages so the usable part goes live first.
Yes, and it is what we recommend most. Pick one well-bounded use case with verifiable answers, run it on real data for a while, then decide on expansion once accuracy and hours saved are measurable.
Some, but not much. Mostly it is keeping the knowledge base current as products, policies or SOPs change. We build the update flow so your team can run it, or fold it into a maintenance agreement and we handle it on a schedule.
Expecting one hundred percent accuracy and designing no path for when it is wrong. Models will be uncertain sometimes; what matters is that they recognise it, hand off to a person, and leave errors visible and traceable. We define what the system should and should not attempt before it goes live.
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