AI Custom Development

Internal-only use (such as internal knowledge bases or agent-assisted support) and "providing generative services to the domestic public" fall under different regulatory requirements. Most internal scenarios carry no public-facing filing obligation, though individual cases leave room for interpretation and we do not treat this as an absolute conclusion. If a system will be opened to the public, we help customers complete a pre-launch compliance assessment during solution design — the filing obligor is the customer.

We manage hallucination as an engineering problem, not a verbal promise: answers must cite sources and can be traced back to the original text; faithfulness is tested against a golden test set before launch; questions outside the knowledge base are answered with a constrained scope or handed off to humans. The goal is to bring errors down to a business-acceptable level where they can be detected — beware of vendors who claim to be completely hallucination-free.

A simple rule: prompt engineering changes behavior, RAG supplies knowledge, and fine-tuning adjusts style and voice. For enterprise knowledge Q&A and policy lookups, RAG is almost always the first step — knowledge updates anytime, answers are source-grounded with citations, and costs are far below fine-tuning. Fine-tuning suits fixed-format outputs and domain voice, usually layered on after RAG as needed. We provide a combined recommendation during solution design.

No. Mainstream Chinese models such as DeepSeek, Qwen (Tongyi Qianwen), GLM (Zhipu), and Kimi are already common choices for enterprise scenarios, and several open-source models support private (on-premises) deployment. We make the model layer swappable, selecting by scenario fit and cost so you are never locked into a single vendor.

Three lines of defense: permissions — RBAC aligned with your org structure and sensitive content isolated by department; deployment — highly sensitive scenarios can use private (on-premises) deployment, where data and models run inside your network without passing through any third party; application — prompt injection protection and output content filtering.

It depends on the number of scenarios, your data foundation, and integration depth. We recommend starting with the POC from our 5-step method: validate effectiveness and cost on real data at small scale, then decide on full investment with real numbers. Tell us your current state and goals for a free initial proposal and quotation.

Operations and iteration is the fifth step of the method, not an option: effectiveness monitoring, knowledge-base operations (unanswered questions iterated weekly), and model and cost reviews are all written into delivery. Annual operations agreements with clear response terms and service boundaries are also available.

Four questions filter out most of them on the spot: first, demand a POC on your real data — no "PowerPoint delivery"; second, demand an evaluation baseline so quality has an objective yardstick; third, demand real production cases — ideally ones where the vendor relies on it in their own product; fourth, be wary of promises like "completely hallucination-free" or "guaranteed filing approval" — the limits of probabilistic models are industry consensus, and teams that state their limits plainly are the more credible ones.