The key to successfully implementing AI tools is not "which model to use," but rather "identifying the highest ROI entry point." Our first step is to assess your processes and identify which tasks are suitable for AI to replace or assist with, and then decide whether to build it in-house or use a ready-made SaaS solution – not every process needs AI, and forcing it in can be a waste of resources.
Need call answering, dispatch or CRM integration? Explore enterprise AI voice support.The most common concern customers have is, "Will the AI give incorrect answers?" Yes, this is inherent in language models – they are naturally prone to fabricating information ("hallucination") to make their responses sound correct. Our approach is not to pretend this problem doesn't exist, but to manage it within acceptable limits using RAG (Retrieval-Augmented Generation): first, break down your product documentation, FAQs, and SOPs into smaller segments and create vector indexes. When a user asks a question, the system first "retrieves information" and then allows the AI to "answer based on the retrieved content," with citations provided. If it cannot answer, we honestly admit, "I'll connect you with a human," rather than making things up. We prioritize transparency: RAG reduces error rates and makes answers traceable, but it doesn't guarantee zero errors. For high-risk questions (financial, legal, medical), we always retain human oversight.
A good AI customer service system is valuable not only for what it can answer, but also for how it handles situations where it cannot. We design escalation rules: if the AI answers incorrectly twice in a row, if the user uses emotional language, or if they ask about sensitive topics like payments and refunds, we automatically transfer the complete conversation to a human, so the customer doesn't have to repeat themselves. The conversation flow is hybrid – fixed processes (checking orders, making reservations, reporting issues) follow rules to ensure accuracy, while open-ended questions are handled by the AI, using intent recognition and multi-turn memory to understand the context (e.g., if the user asks "How long will it take to return?", then "How many days?", the system understands that both questions refer to the same thing). Pure decision trees are too rigid, and letting the AI run completely wild can lead to unpredictable results. The best approach is to find a balance.
A common situation is that business owners see news about AI and think, "We should implement it too," but they don't know what problem it will solve. Our approach is the opposite: we first identify the "repetitive, labor-intensive, and rule-based" areas where the ROI is most easily calculated. For example, we implemented AI voice answering for a taxi fleet in Hualien, where a large number of calls were handled by repetitive human labor. The AI can handle both immediate voice conversations and automatically dispatch vehicles based on pre-defined rules. This is a cost-effective solution. On the other hand, if you simply want a "impressive-looking" AI, we will honestly tell you that it's not worth the investment.
We have delivered AI applications, including the AI phone answering and SmartDispatcherV2 backend for GoGoCha Hualien taxis, multi-model deployment of Claude/Gemini/OpenAI for the dating game Alive, and the 7 emotional support system, which are available on iOS/Android; as well as LINE appointment systems for traditional Chinese medicine clinics (including concurrent control and a large number of E2E tests). These are real systems that are currently in operation, not demos.
The actual steps we take when implementing AI tools for businesses.
Assess your workflows and identify the repetitive, labor-intensive, rule-based areas with the highest ROI as starting points for AI implementation.
Based on data sensitivity, integration needs, and cost, decide whether to use cloud APIs, open-source self-hosting, or existing SaaS.
Develop a minimum viable product (MVP) that can be tested. You can usually see a prototype within two weeks, and early validation is possible.
Get feedback from actual users and adjust prompts, knowledge bases, and interaction flows accordingly.
Integrate with existing systems, deploy, and provide source code and operational instructions.
All packages include complete service content, with no hidden fees.
Suitable for:Validation Process and Use Cases
Suitable for:Companies with clear process and data integration needs
