Turn your company documents, SOPs, and product and pricing data into a queryable knowledge base — ask a question and find the answer along with its source, helping new hires get up to speed quickly.
IT Services
Enterprise AI Adoption & On-Premise Deployment
Bring AI into your company's real workflows: enterprise knowledge-base Q&A, AI customer service, document processing, and coding assistance. Based on data sensitivity, we evaluate cloud, on-premise (private), or hybrid deployment — sensitive data can stay in your own server room, with permissions and guardrails for safe, manageable control. Based in Kaohsiung, we take on custom AI projects.
Application scenarios
AI isn't a gimmick — it's a tool that saves you time.
First identify the truly repetitive, time-consuming, or bottlenecked steps in your company, then use AI to solve them — starting with a scenario that makes a difference and scaling up once you see results.
Automated FAQ replies, initial reception, and form compilation, integrated with LINE, your website, or your customer-service inbox, reducing repetitive manual responses.
Extracting, compiling, and drafting contracts, quotations, and reports — let AI lay the groundwork for tedious paperwork, then have a person confirm it, saving a great deal of time.
AI assistance for programming, data conversion, and process automation, connected to your existing systems and workflows so repetitive work runs on its own.
Application assessment combining surveillance and image recognition — such as foot traffic, license plate or anomaly detection — turning video into usable information.
Dedicated AI tools and dashboards tailored to each department, consolidating scattered data and steps into a single, easy-to-use interface.
Deployment method
Cloud, on-premise, or hybrid — decided by your data sensitivity.
Not everything has to go to the cloud, and not everything has to be self-built. Based on the use case, data sensitivity, and budget, we recommend the most suitable approach or a mix of them.
Go live quickly with mainstream models on a pay-as-you-go basis — ideal for general, non-confidential use, with fast deployment and low upfront cost.
Deploying open-source models (Qwen, Llama, etc.) in your own server room or on a standalone machine keeps data inside the company — ideal for sensitive data, internal audit, and compliance requirements.
Run general tasks in the cloud and confidential tasks on-premise, striking a balance among cost, performance and security, and scaling up gradually.
On-premise deployment can be combined with model fine-tuning (on industry-specific corpora), account permissions, and content guardrails, and aligned with the OWASP LLM risk list — so AI can be used with confidence and stays manageable.
Implementation process
From assessment to go-live, step by step.
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1
Requirements and data assessment
Clarify the scenario you want to solve, your data sources and their sensitivity, and determine what is suitable for the cloud and what should stay on-premise.
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2
Scenario selection and PoC
First pick one genuinely impactful scenario for small-scale validation, confirm the results and cost, then decide whether to scale up.
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3
Deployment & Integration
Complete cloud or on-premise deployment, integrating existing systems, knowledge bases and permissions, with content guardrails added.
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4
Launch and optimization
Training, performance review and continuous adjustment — keeping what works and removing what doesn't.
FAQ
Frequently asked questions before adopting AI.
We're a small company — is AI still a good fit for us?
Yes, it's a good fit. Start small with a scenario that genuinely makes a difference (such as knowledge-base Q&A or AI customer service), then scale up once you see results — there's no need for a big investment up front.
Our data is confidential — can we avoid the cloud?
Yes. On-premise / private deployment places open-source models (such as Qwen, Llama) in your own server room or on a standalone machine, keeping data inside the company with no leakage — ideal for sensitive data or compliance requirements.
Roughly how much does adopting AI cost, and how long does it take?
It depends on the scenario and deployment method. Cloud APIs can go live quickly with pay-as-you-go billing; on-premise involves a one-time hardware cost. We first take stock of your needs and data, then provide a scope and quotation.
Could AI say the wrong thing or leak data?
We combine content guardrails, access control, and data-boundary settings, aligned with the OWASP LLM risk list, to reduce the risk of hallucinations and data leaks.
We're already using ChatGPT — can you help integrate it into our company systems?
Yes. We connect AI to your knowledge base, customer service, forms, or existing systems to build a tool that truly fits your workflow — not just a chat box.
Want to use AI to solve a specific problem in your company?