We connect AI to the systems you already run
Agents, RAG knowledge bases, support, BI and on-premise LLMs, wired into your ERP, CRM, POS and messaging. Prove it works with a 2–4 week PoC, then scale.
You might be stuck here
Most Taiwanese companies have adopted or planned AI; only about one in ten has it running inside daily operations. Tools aren't the shortage — people who can connect them to existing systems are.
Plenty of tools, none of them connected
- One tool for support, another for reporting, another for the knowledge base.
- Each with its own logins and its own copy of the data.
- The same customer is three different people across three systems.
How we handle itAudit first, then connect: direct API where one exists, a middleware layer or RPA where it doesn't.
Nobody available who can build it
- You need someone who can wire AI into an ERP and a CRM.
- There are very few of them, and growing one in-house takes longer.
- Buying another tool doesn't solve this.
How we handle itThis is an integration team you can hire by the project — staffed within one PoC cycle.
Worried about data leaving
- Quotes, customer lists and contracts can't go to an outside service.
- Nobody will say what on-premise costs or what hardware it needs.
- Nor how far the compliance requirements actually go.
How we handle itTiered by sensitivity. At the strictest tier everything runs on-premise and no data leaves your building.
PoC pricing
A quote is made of these four parts
- Scope and data Interviews, a data-source audit, success criteria written down.
- Build and integration Prototype build, connecting your existing systems, data cleaning.
- Testing and acceptance Real cases measured, regression runs, accuracy tuning.
- Handover and training Documentation, training, monitoring setup, source code delivered.
Software licences and model usage are listed separately and billed on actual consumption.
AI agent rollout
Usually includes:A working agent prototype for one scenario in 2–4 weeks, wired to real data with measured results.
Affects the quote:Number of processes automated, systems connected, complexity of the decision logic.
Final price is confirmed once the scope is clear · First consultation is free
RAG knowledge base
Usually includes:One knowledge base, live in 4 weeks, with a basic question-and-answer interface.
Affects the quote:Number of sources, document volume and how much cleaning they need, custom interface or not.
Final price is confirmed once the scope is clear · First consultation is free
Conversational support
Usually includes:Connected to your messaging account and website chat, with automated FAQ answers and handover to a human.
Affects the quote:Number of channels, monthly message volume, voice QA. Model usage after launch is billed separately.
Final price is confirmed once the scope is clear · First consultation is free
Private / on-premise LLM
Usually includes:Tiers from RAG data that never leaves, through VPC deployment, to fully on-premise with fine-tuning. GPU hardware not included.
Affects the quote:Privacy tier, data volume and fine-tuning needs, whether GPU hardware is included.
Final price is confirmed once the scope is clear · First consultation is free
PoC fees can be credited against the production project. Normal deployment is not charged separately — only on-premise model hosting and model training are.
How it works
The PoC takes 2–4 weeks on a fixed scope to prove one scenario works, keeping the risk small.
- 01
Scope and data
- Pick one scenario with a visible payoff.
- Fix the scope, the data sources and the success criteria.
- Confirm the data is actually reachable.
- 02
PoC build
- A testable prototype running on your real data.
- Progress shown to you every two weeks.
- No extra charges inside the agreed scope.
- 03
Measurement
- Real cases measured into numbers.
- A clear verdict on whether it's worth scaling.
- PoC fees credited against the production project.
- 04
Into production
- Production deployment and monitoring.
- Training and written procedures.
- Source code and data delivered to you.
Common questions
Why start with a PoC instead of building the whole thing?
The PoC proves one scenario works in 2–4 weeks on a fixed scope, which keeps the risk small. If it's worth it, then you scale.
Is the PoC money wasted if we stop there?
No. The PoC fee is credited in full or in part against the production project, and you keep a working prototype plus a measured conclusion.
Our systems are old and our data is scattered. Can it still be connected?
Audit first, then connect. Direct API where one exists; a middleware layer or RPA where it doesn't.
Will we be locked into one vendor's model?
We don't bet on a single model or platform. Selection is driven by the scenario, the cost and the data sensitivity, and the architecture is built so it can be swapped later.
If we hand data to the AI, can it leak?
Tiered by sensitivity: RAG data that never leaves, deployment inside your own VPC, or at the strictest tier fully on-premise with a private model.